US 8,312,017 B2Grant

Recommender system for identifying a new set of media items responsive to an input set of media items and knowledge base metrics

Issue Date:2012-11-13

•18 Claims

•5 Drawing Sheets

Abstract

Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Metadata

Assignee

Apple Inc.

Inventors

Francisco J. Martin
Jim Shur
Marc Torrens

Application Information

Application Number:US 12/685,639

Filing Date:2010-01-11

Priority Date:2005-02-03

Art Unit:2162

Classifications

IPC:

G06F17/30

Patent Drawings (5 sheets)

Description

RELATED APPLICATIONS

[0001] This application claims priority from pending U.S. patent application Ser. No. 11/346,818, filed Feb. 3, 2006, which claims benefit from U.S. Provisional Application No. 60/649,987 filed Feb. 3, 2005, all of which is incorporated herein by this reference.

COPYRIGHT NOTICE

[0002] © 2005-2006 MusicStrands, Inc. A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 37 CFR §1.71(d).

TECHNICAL FIELD

[0003] This invention relates generally to systems and methods for recommending media items to a user in a personalized manner. It particularly relates to “recommender” computer software systems for media items which are grouped by end users to define mediasets.

BACKGROUND OF THE INVENTION

[0004] New technologies combining digital media item players with dedicated software, together with new media distribution channels through computer networks (e.g., the Internet) are quickly changing the way people organize and play media items. As a direct consequence of such evolution in the media industry, users are faced with a huge volume of available choices that clearly overwhelm them when choosing what item to play in a certain moment.

[0005] This overwhelming effect is apparent in the music arena, where people are faced with the problem of selecting music from very large collections of songs. However, in the future, we might detect similar effects in other domains such as music videos, movies, news items, etc.

[0006] In general, our invention is applicable to any kind of media item that can be grouped by users to define mediasets. For example, in the music domain, these mediasets are called playlists. Users put songs together in playlists to overcome the problem of being overwhelmed when choosing a song from a large collection, or just to enjoy a set of songs in particular situations. For example, one might be interested in having a playlist for running, another for cooking, etc.

[0007] Different approaches can be adopted to help users choose the right options with personalized recommendations. One kind of approach is about using human expertise to classify the media items and then use these classifications to infer recommendations to users based on an input mediaset. For instance, if in the input mediaset the item x appears and x belongs to the same classification as y, then a system could recommend item y based on the fact that both items are classified in a similar cluster. However, this approach requires an incredibly huge amount of human work and expertise. Another approach is to analyze the data of the items (audio signal for songs, video signal for video, etc) and then try to match users preferences with the extracted analysis. This class of approaches is yet to be shown effective from a technical point of view.

[0008] Hosken (U.S. Pat. No. 6,438,579) describes a system and method for recommending media items responsive to query media items based on the explicit and implicit user characterizations of the content of the media items. Dunning, et. al. (U.S. Patent Application Pubs 2002/0082901 and 2003/0229537) disclose a system and method for discovering relationships between media items based on explicit and implicit user associations between said items. The present invention differs from Hosken and Dunning, et. al. in that it provides an automatic way to discover relationships between media items without requiring any user rating or any other knowledge from the user.

[0009] Lazarus et. al. (U.S. Pat. No. 6,134,532) describe a system and method for providing individually targeted advertising items to users based on observations of user behavior as quantified by representations for important content words in text-bearing materials accessed by a user. Advertising items responsive to the observed behavior of a user are identified by explicitly characterizing content words in the advertisement and by further characterizing candidate responsive items selected in this manner based on observations of user responses to them. Behrens et al. (U.S. Pat. No. 6,615,208) describe a method for associating textual materials similar to that of Lazarus et. al. for recommending items for purchase to a user based on the observed purchasing behavior of the user. The present invention differs from Lazarus et. al. and Behrens et. al. in that it is not based on user behavior other than how users in general associate items to form mediasets. Furthermore, Lazarus et al. and Behrens et. al. use standard methods for analyzing textual materials and the associated vector-space techniques as the basis for quantifying associations between textual representations of user behavior and textual content of advertising items while the present invention uses novel variants of associational methods for media items as the basis for associating media items.

[0010] Aggarwal and Yu (U.S. Pat. No. 6,487,539) describe a system for providing product recommendations to users based on extracting characterizations of products from textual descriptive materials about product features. Users are clustered with others users based on observed purchasing behaviors and the extracted descriptions of products purchased by a cluster of users are matched to extracted descriptions of other products and most similar products recommended to a user that is associated with that cluster of users. The present invention differs from Aggarwal and Yu in that the associations among media items are not based on characterizations of the items themselves, but rather on how they are grouped together.

[0011] Robinson (U.S. Pat. No. 5,918,014) describes a system and a process for presenting users of the World Wide Web with targeted advertising. Users are clustered based on observed web browsing of sites and purchasing activities while advertisements of probable interest to the users in a cluster are identified by presenting ads to users identified with the cluster and observing the interest of users in the advertisement as expressed by exploration of the ad and purchasing activity. Ads are selected for presentation to a specific user by first associating the user with an identified community, and then selecting from the ads associated with that community those, which conform more closely to the specific characteristics of the user, utilized to associate the user with the community. The present invention differs from Robinson in that media items are not identified for potential presentation based on the acceptance of the media item in response to random presentation to a group of users representative of the user, media items for presentation are instead affirmatively identified by the media items they contain.

BRIEF SUMMARY OF PRESENTLY PREFERRED EMBODIMENTS

[0012] This invention addresses the problem of assisting users in building their mediasets by recommending media items that go well together with an initial (or input) mediaset. The recommendation is computed using metrics among the media items of a knowledge base of the system. This knowledge base comprises collections of mediasets from a community of users. (As explained below, a mediaset is not a collection of media items or content. Rather, it is a list of such items, and may include various metadata.) Preferably, the methods of the present invention are implemented in computer software.

[0013] In commercial applications, the invention can be deployed in various ways. Recommender services can be provided, for example, to remote users of client computing machines via a network of almost any kind, wired or wireless. Here we use “computing machines” to include traditional computers, as well as cell phones, PDA's, portable music players etc. The knowledge base of the system, a database, can be local or remote from the user. It may be at one location or server, or distributed in various ways.

[0014] The invention in one aspect embodies a system for identifying a set of media items in response to an input set of media items. The system requires a knowledge base consisting of a collection of mediasets. Mediasets are sets of media items, which are naturally grouped by users. They reflect the users subjective judgments and preferences. The mediasets of the knowledge base define metrics among items. Such metrics indicate the extent of correlation among media items in the mediasets of the knowledge base.

[0015] Various different metrics between and among media items can be generated from the knowledge base of mediasets. Such metrics can include but are not limited to the follow examples:

- [0016] a) Pre-concurrency (for ordered mediasets) between two items is computed as the number of times a given item precedes the other item in the mediasets of the knowledge base.
- [0017] b) Post-concurrency (for ordered mediasets) between two items is computed as the number of times an item follows another item in the mediasets of the knowledge base.
- [0018] c) Co-concurrency between two items is computed as the number of times the items appear together in a mediaset.
- [0019] d) Metadata similarities may be computed as well by considering keywords associated with the media items such as artist, actor, date, etc.
- [0020] e) Combinations of the previous metrics can be useful.
- [0021] f) Combinations of the previous metrics applying transitivity.

[0022] Such metrics can be represented in an explicit form that directly associates media items with other media items. For each media item of the input set, the system retrieves n media items with highest metrics. These media items are called candidates. Then, the recommended set of media items is a subset of the candidates that maximize an optimization criterion. Such criterion can be simply defined using the metrics of the knowledge base of the system. Furthermore, such criterion can also include filters including but not limited to:

- [0023] a) Filters that the user expresses to focus the recommendation only on a determined type of items.
- [0024] b) Filters that the user expresses to focus the recommendations on items that meet certain keyword-based criteria, such as a specific artist's, year/s, genre/s, etc.
- [0025] c) Filters that personalize the recommendations to the user. This kind of filtering includes recommending only items that the user knows about, or only items that the user does not know about, etc.

[0026] Additional aspects and advantages will be apparent from the following detailed description of preferred embodiments, which proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] FIG. 1A is a representation in matrix form of a metric describing the similarity values between collections of media items.

[0028] FIG. 1B provides a weighted graph representation for the associations within a collection of media items. Each edge between two media items is annotated with a weight representing the value of the metric for the similarity between the media items.

[0029] FIG. 2 is a block diagram of one method for selecting a set of media items corresponding to an initial set of media items in accordance with an embodiment of the invention.

[0030] FIG. 3 is a simplified, conceptual diagram of a knowledge base or database comprising a plurality of mediasets.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0031] Reference is now made to the figures in which like reference numerals refer to like elements. For clarity, the first digit of a reference numeral indicates the figure number in which the corresponding element is first used.

[0032] In the following description, certain specific details of programming, software modules, user selections, network transactions, database queries, database structures, etc. are omitted to avoid obscuring the invention. Those of ordinary skill in computer sciences will comprehend many ways to implement the invention in various embodiments, the details of which can be determined using known technologies.

[0033] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In general, the methodologies of the present invention are advantageously carried out using one or more digital processors, for example the types of microprocessors that are commonly found in servers, PC's, laptops, PDA's and all manner of desktop or portable electronic appliances.

Definitions

[0034] The system preferably comprises or has access to a knowledge base which is a collection of mediasets. A mediaset is a list of media items that a user has grouped together. A media item can be almost any kind of content; audio, video, multi-media, etc., for example a song, a book, a newspaper or magazine article, a movie, a piece of a radio program, etc. Media items might also be artists or albums. If a mediaset is composed of a single type of media items it is called a homogeneous mediaset, otherwise it is called a heterogeneous mediaset. A mediaset can be ordered or unordered. An ordered mediaset implies a certain order with respect to the sequence in which the items are used¹by the user. Note again that a mediaset, in a preferred embodiment, is a list of media items, i.e. meta data, rather than the actual content of the media items. In other embodiments, the content itself may be included. Preferably, a knowledge base is stored in a machine-readable digital storage system. It can employ well-known database technologies for establishing, maintaining and querying the database. ¹Depending on the nature of the item, it will be played, viewed, read, etc.

[0035] In general, mediasets are based on the assumption that users group media items together following some logic or reasoning, which may be purely subjective, or not. For example, in the music domain, a user may be selecting a set of songs for driving, hence that is a homogeneous mediaset of songs. In this invention, we also consider other kinds of media items such as books, movies, newspapers, and so on. For example, if we consider books, a user may have a list of books for the summer, a list of books for bus riding, and another list of books for the weekends. A user may be interested in expressing a heterogeneous mediaset with a mix of books and music, expressing (impliedly) that the listed music goes well with certain books.

[0036] A set of media items is not considered the same as a mediaset. The difference is mainly about the intention of the user in grouping the items together. In the case of a mediaset the user is expressing that the items in the mediaset go together well, in some sense, according to her personal preferences. A common example of a music mediaset is a playlist. On the other hand, a set of media items does not express necessarily the preferences of a user. We use the term set of media items to refer to the input of the system of the invention as well as to the output of the system.

[0037] A metric M between a pair of media items i and j for a given knowledge base k expresses some degree of relation between i and j with respect to k. A metric may be expressed as a “distance,” where smaller distance values (proximity) represent stronger association values, or as a similarity, where larger similarity values represent stronger association values. These are functionally equivalent, but the mathematics are complementary. The most immediate metric is the co-concurrency (i, j, k) that indicates how many times item i and item j appear together in any of the mediasets of k. The metric pre-concurrency (i, j, k) indicates how many times item i and item j appear together but i before j in any of the mediasets of k. The metric post-concurrency (i, j, k) indicates how many times item i and item j appear together but only i after j in any of the mediasets of k. The previous defined metrics can also be applied to considering the immediate sequence of i and j. So, the system might be considering co/pre/post-concurrencies metrics but only if items i and j are consecutive in the mediasets (i.e., the mediasets are ordered). Other metrics can be considered and also new ones can be defined by combining the previous ones.

[0038] A metric may be computed based on any of the above metrics and applying transitivity. For instance, consider co-concurrency between item i and j, co(i,j), and between j and k, co(j,k), and consider that co(i,k)=0. We could create another metric to include transitivity, for example d(i,k)=1/co(i,j)+1/co(j,k). These type of transitivity metrics may be efficiently computed using standard branch and bound search algorithms. This metric reveals an association between items i and k notwithstanding that i and k do not appear within any one mediaset in K.

[0039] A matrix representation of metric M, for a given knowledge base K can be defined as a bidimensional matrix where the element M(i,j) is the value of the metric between the media item i and media item j.

[0040] A graph representation for a given knowledge base k, is a graph where nodes represent media items, and edges are between pairs of media items. Pairs of media items i, j are linked by labeled directed edges, where the label indicates the value of the similarity or distance metric M(i,j) for the edge with head media item i and tail media item j.

PREFERRED EMBODIMENTS OF THE INVENTION

[0041] One embodiment of the invention is illustrated by the flow diagram shown in FIG. 2. This method accepts an input set 301 of media items. Usually, this is a partial mediaset, i.e. a set of media items (at lease one item) that a user grouped together as a starting point with the goal of building a mediaset. A first collection of candidate media items most similar to the input media items is generated by process 302 as follows.

[0042] As a preliminary matter, in a presently preferred embodiment, a pre-processing step is carried out to analyze the contents of an existing knowledge base. This can be done in advance of receiving any input items. As noted above, the knowledge base comprises an existing collection of mediasets. This is illustrated in FIG. 3, which shows a simplified conceptual illustration of a knowledge base 400. In FIG. 3, the knowledge base 400 includes a plurality of mediasets, delineated by rectangles [or ovals] and numbered 1 through 7. Each mediaset comprises at least two media items. For example, mediaset 2 has three items, while mediaset 7 has five items. The presence of media items within a given mediaset creates an association among them.

[0043] Pre-processing analysis of a knowledge base can be conducted for any selected metric. In general, the metrics reflect and indeed quantify the association between pairs of media items in a given knowledge base. The process is described by way of example using the co-concurrency metric mentioned earlier. For each item in a mediaset, the process identifies every other item in the same mediaset, thereby defining all of the pairs of items in that mediaset. For example, in FIG. 3, one pair in set 1 is the pair M(1,1)+M(1,3). Three pairs are defined that include M(1,1). This process is repeated for every mediaset in the knowledge base, thus every pair of items that appears in any mediaset throughout the knowledge base is defined.

[0044] Next, for each pair of media items, a co-concurrency metric is incremented for each additional occurrence of the same pair of items in the same knowledge base. For example, if a pair of media items, say the song “Uptown Girl” by Billy Joel and “Hallelujah” by Jeff Buckley, appear together in 42 different mediasets in the knowledge base (not necessarily adjacent one another), then the co-concurrency metric might be 42 (or some other figure depending on the scaling selected, normalization, etc. In some embodiments, this figure or co-concurrency “weight” may be normalized to a number between zero and one.

[0045] Referring now to FIG. 1A, matrix 100 illustrates a useful method for storing the metric values or weights for any particular metric. Here, individual media items in the knowledge base, say m₁, m₂, m₃. . . m_kare assigned corresponding rows and columns in the matrix. In the matrix, the selected metric weight for every pair of items is entered at row, column location x,y corresponding to the two media items defining the pair. In FIG. 1A, the values are normalized.

[0046] Now we assume an input set of media items is received. Referring again to process step 302, a collection of “candidate media items” most similar to the input media items is generated, based on a metric matrix like matrix 100 of FIG. 1A. For instance, for each media item, say (item m₂) in the input set 301, process 302 could add to a candidate collection of media items every media item (m₁, m₃. . . m_kin FIG. 1A) that has a non-zero similarity value, or exceeds a predetermined threshold value, in the corresponding row 102 of metric matrix 100 for the media item m₂, labeling each added media item with the corresponding metric value (0.7, 0.4 and 0.1, respectively). See the edges in FIG. 1B. For each media item in the input set of size m, process 302 selects n media items as candidates; thus the aggregation of all the candidates produces a set of at most m*n media items.

[0047] Process 303 receives the candidate set from process 302 which contains at the most m*n media items. This component selects p elements from the m*n items of the candidate set. This selection can be done according to various criteria. For example, the system may consider that the candidates should be selected according to the media item distribution that generated the candidate set. This distribution policy may be used to avoid having many candidates coming from very few media items. Also, the system may consider the popularity of the media items in the candidate set. The popularity of a media item with respect to a knowledge base indicates the frequency of such media item in the mediasets of the knowledge base.

[0048] Finally, from the second collection of [p] media items, a third and final output set 305 of some specified number of media items is selected that satisfy any additional desired external constraints by a filter process 304. For instance, this step could ensure that the final set of media items is balanced with respect to the metrics among the media sets of the final set. For example, the system may maximize the sum of the metrics among each pair of media items in the resulting set. Sometimes, the system may be using optimization techniques when computation would otherwise be too expensive. Filtering criteria such as personalization or other preferences expressed by the user may also be considered in this step. In some applications, because of some possible computational constraints, these filtering steps may be done in the process 303 instead of 304. Filtering in other embodiments might include genre, decade or year of creation, vendor, etc. Also, filtering can be used to demote, rather then remove a candidate output item.

[0049] In another embodiment or aspect of the invention, explicit associations including similarity values between a subset of the full set of media items known to the system, as shown in graph form in FIG. 1B, may be used. To illustrate, if the similarity value between a first media item 202, generally denoted below by the index i, and a second media item, say 214, generally denoted below by the index j, is not explicitly specified, an implicit similarity value can instead be derived by following a directed path such as that represented by edges 210 and 212 from the first media item to an intermediate item, and finally to the second media item of interest, in this example item m_p. Any number of intermediate items can be traversed in this manner, which we call a transitive technique. The list of similarity values M(i, i+1), M(i+1, i+2), M(i+k, j) between pairs of media items along this path through the graph are combined in a manner such that the resulting value satisfies a definition of similarity between media item i and media item j appropriate for the application. For example, the similarity M(i,j) might be computed as:
M(i,j)=min{M(i,i+1),M(i,i+2), . . . , M(i+k,j)}
or
M(i,j)=M(i,i+1)*M(i,i+2)* . . . *M(i+k,j)

[0050] Other methods for computing a similarity value M(i,j) for the path between a first media item i and a second, non-adjacent media item j where the edges are labeled with the sequence of similarity values M(i, i+1), M(i+1, i+2), . . . , M(i+k, j) can be used. From the user standpoint, this corresponds to determining an association metric for a pair of items that do not appear within the same mediaset.

[0051] Many modifications and other embodiments of the invention will come to mind to one skilled in the art to which this invention pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. For example, one of ordinary skill in the art will understand that, while the above system and methods were described as embodied in a media recommendation system, it should be understood that the inventive system could be used in any system for recommending other items that can be grouped by users following some criterion. Although specific terms are employed herein, there are used in a generic and descriptive sense only and not for purposes of limitation.

[0052] It will be obvious to those having skill in the art that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. The scope of the present invention should, therefore, be determined only by the following claims.

Claims

The invention claimed is:

1. A computer-implemented method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing a knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

for an input media item, computing a corresponding value of a selected metric relative to each one of a plurality of media items in the knowledge base, wherein computing comprises computing a corresponding metric value for each input item relative to each one of a plurality of media items in the knowledge base by applying a co-concurrency metric responsive to a number of times that the respective input media item appears together with the corresponding media item in the same mediaset in the knowledge base;

responsive to the computed metric values, selecting at least one of the knowledge base media items as a candidate output media item;

selecting among the candidate output media items to form the output set of media items, wherein selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

delivering the output set of media items to a user.

2. The method according to claim 1 wherein the input set of media items comprises first meta data identifying songs.

3. The method according to claim 2 wherein the output set of media items comprises second meta data identifying songs.

4. The method according to claim 1 wherein:

selecting among the candidate output media items to form the output set of media items comprises applying a predetermined selection criterion responsive to the media item distribution from which the candidate output media items were selected.

5. The method according to claim 1 wherein:

the input set of media items comprises first meta data identifying songs;

the knowledge base comprises a plurality of collected mediasets of songs; and

the output set of media items comprises second meta data identifying songs.

6. The method according to claim 5 wherein delivering the output set of media items to a user comprises displaying or transmitting the second meta data to the user.

7. The method according to claim 6 and further comprising filtering the selected candidate output media items to apply a predetermined additional external constraint to form the output set of media items.

8. The method according to claim 7 wherein filtering comprises maximizing the sum of the selected metric values among each pair of media items included in selecting the output set.

9. The method according to claim 7 wherein filtering comprises applying a predetermined preference or criterion selected by the user.

10. A method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing a knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

selecting among the knowledge base media items the items having the highest computed metric values as candidate output media items, wherein selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

selecting among the candidate output media items to form the output set of media items.

11. The method according to claim 10 wherein the selected metric comprises one of pre-concurrency metric, a post-concurrency metric and a co-concurrency metric.

12. The method according to claim 10 wherein:

the input set of media items comprises first meta data identifying songs;

the knowledge base comprises a plurality of collected mediasets of songs; and

the output set of media items comprises second meta data identifying songs.

13. The method according to claim 12 and further comprising filtering the selected candidate output media items to form the output set of media items.

14. The method according to claim 13 wherein filtering applies a filter selected by a user to limit the output set of media items to a determined type of items.

15. The method according to claim 13 wherein filtering applies a filter selected by a user to limit the output set of media items to items that meet defined keyword-based meta data criteria, thereby personalizing the recommendations.

16. A computer-implemented method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item from a first user;

accessing a knowledge base comprising a plurality of collected mediasets from a plurality of other users, each knowledge base mediaset comprising at least one media item;

responsive to the computed metric values, selecting at least one of the knowledge base media items as a candidate output media item;

delivering the output set of media items to the first user.

17. A computer-implemented method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing an ordered knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

for an input media item, computing a corresponding value of a selected metric relative to each one of a plurality of media items in the knowledge base, wherein the step of computing includes computing a corresponding metric value for each input item relative to each one of a plurality of media items in the knowledge base by applying at least one of a pre-concurrency metric and post-concurrency metric;

responsive to the computed metric values, selecting at least one of the knowledge base media items as a candidate output media item;

selecting among the candidate output media items to form the output set of media items, wherein selecting a candidate output media item comprises selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

delivering the output set of media items to a user.

18. A method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing an ordered knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

selecting among the knowledge base media items the items having the highest computed metric values as candidate output media items, wherein selecting a candidate output media item comprises selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

selecting among the candidate output media items to form the output set of media items.

Patent Citations (284)

Patent	Date	Inventor	Cited By
US4996642(A)	1991-02-01	Hey	Applicant
US5355302(A)	1994-10-01	Martin	Applicant
US5375235(A)	1994-12-01	Berry	Applicant
US5464946(A)	1995-11-01	Lewis	Applicant
US5483278(A)	1996-01-01	Strubbe	Applicant
US5583763(A)	1996-12-01	Atcheson	Applicant
US5724521(A)	1998-03-01	Dedrick	Applicant
US5754939(A)	1998-05-01	Herz	Applicant
US5758257(A)	1998-05-01	Herz	Applicant
US5765144(A)	1998-06-01	Larche	Applicant
US5890152(A)	1999-03-01	Rapaport	Applicant
US5918014(A)	1999-06-01	Robinson	Applicant
US5926624(A)	1999-07-01	Katz et al.	Examiner
US5950176(A)	1999-09-01	Keiser	Applicant
US6000044(A)	1999-12-01	Chrysos	Applicant
US6047311(A)	2000-04-01	Ueno	Applicant
US6112186(A)	2000-08-01	Bergh	Applicant
US6134532(A)	2000-10-01	Lazarus	Applicant
US6345288(B1)	2002-02-01	Reed	Applicant
US6346951(B1)	2002-02-01	Mastronardi	Applicant
US6347313(B1)	2002-02-01	Ma	Applicant
US6349339(B1)	2002-02-01	Williams	Applicant
US6381575(B1)	2002-04-01	Martin	Applicant
US6430539(B1)	2002-08-01	Lazarus	Applicant
US6434621(B1)	2002-08-01	Pezzillo	Applicant
US6438579(B1)	2002-08-01	Hosken	Applicant
US6487539(B1)	2002-11-01	Aggarwal	Applicant
US6526411(B1)	2003-02-01	Ward	Applicant
US6532469(B1)	2003-03-01	Feldman	Applicant
US6577716(B1)	2003-06-01	Minter	Applicant
US6587127(B1)	2003-07-01	Leeke	Applicant
US6615208(B1)	2003-09-01	Behrens	Applicant
US6647371(B2)	2003-11-01	Shinohara	Applicant
US6687696(B2)	2004-02-01	Hofmann	Applicant
US6690918(B2)	2004-02-01	Evans	Applicant
US6704576(B1)	2004-03-01	Brachman	Applicant
US6727914(B1)	2004-04-01	Gutta	Applicant
US6748395(B1)	2004-06-01	Picker	Applicant
US6751574(B2)	2004-06-01	Shinohara	Applicant
US6785688(B2)	2004-08-01	Abajian	Applicant
US6842761(B2)	2005-01-01	Diamond	Applicant
US6850252(B1)	2005-02-01	Hoffberg	Examiner
US6914891(B2)	2005-07-01	Ha	Applicant
US6931454(B2)	2005-08-01	Deshpande	Applicant
US6933433(B1)	2005-08-01	Porteus	Applicant
US6941324(B2)	2005-09-01	Plastina	Applicant
US6947922(B1)	2005-09-01	Glance	Applicant
US6950804(B2)	2005-09-01	Strietzel	Applicant
US6987221(B2)	2006-01-01	Platt	Applicant
US6990497(B2)	2006-01-01	O'Rourke	Applicant
US6993532(B1)	2006-01-01	Platt et al.	Examiner
US7013238(B1)	2006-03-01	Weare	Applicant
US7020637(B2)	2006-03-01	Bratton	Applicant
US7021836(B2)	2006-04-01	Anderson	Applicant
US7043479(B2)	2006-05-01	Ireton	Applicant
US7051352(B1)	2006-05-01	Schaffer	Applicant
US7072846(B1)	2006-07-01	Robinson	Applicant
US7082407(B1)	2006-07-01	Bezos	Applicant
US7096234(B2)	2006-08-01	Plastina	Applicant
US7111240(B2)	2006-09-01	Crow	Applicant
US7113917(B2)	2006-09-01	Jacobi	Applicant
US7113999(B2)	2006-09-01	Pestoni	Applicant
US7120619(B2)	2006-10-01	Drucker	Applicant
US7127143(B2)	2006-10-01	Elkins, II	Applicant
US7136866(B2)	2006-11-01	Springer, Jr.	Applicant
US7139723(B2)	2006-11-01	Conkwright	Applicant
US7174126(B2)	2007-02-01	McElhatten	Applicant
US7180473(B2)	2007-02-01	Horie	Applicant
US7194421(B2)	2007-03-01	Conkwright	Applicant
US7196258(B2)	2007-03-01	Platt	Examiner
US7197472(B2)	2007-03-01	Conkwright	Applicant
US7224282(B2)	2007-05-01	Terauchi	Applicant
US7236941(B2)	2007-06-01	Conkwright	Applicant
US7246041(B2)	2007-07-01	Fukuda	Applicant
US7256341(B2)	2007-08-01	Plastina	Applicant
US7277870(B2)	2007-10-01	Mourad	Applicant
US7302419(B2)	2007-11-01	Conkwright	Applicant
US7302468(B2)	2007-11-01	Wijeratne	Applicant
US7358434(B2)	2008-04-01	Plastina	Applicant
US7363314(B2)	2008-04-01	Picker	Applicant
US7392212(B2)	2008-06-01	Hancock	Applicant
US7403769(B2)	2008-07-01	Kopra	Applicant
US7415181(B2)	2008-08-01	Greenwood	Applicant
US7434247(B2)	2008-10-01	Dudkiewicz	Applicant
US7457852(B2)	2008-11-01	O'Rourke et al.	Examiner
US7457862(B2)	2008-11-01	Hepworth	Applicant
US7478323(B2)	2009-01-01	Dowdy	Applicant
US7487107(B2)	2009-02-01	Blanchard et al.	Applicant
US7490775(B2)	2009-02-01	Biderman	Applicant
US7493572(B2)	2009-02-01	Card	Applicant
US7499630(B2)	2009-03-01	Koch	Applicant
US7505959(B2)	2009-03-01	Kaiser	Applicant
US7546254(B2)	2009-06-01	Bednarek	Applicant
US7568213(B2)	2009-07-01	Carhart	Applicant
US7571121(B2)	2009-08-01	Bezos	Applicant
US7571183(B2)	2009-08-01	Renshaw	Applicant
US7574422(B2)	2009-08-01	Guan	Applicant
US7574513(B2)	2009-08-01	Dunning	Applicant
US7580932(B2)	2009-08-01	Plastina	Applicant
US7599847(B2)	2009-10-01	Block et al.	Applicant
US7599906(B2)	2009-10-01	Kashiwagi	Applicant
US7599950(B2)	2009-10-01	Walther	Applicant
US7644077(B2)	2010-01-01	Picker	Applicant
US7647613(B2)	2010-01-01	Drakoulis	Applicant
US7650570(B2)	2010-01-01	Torrens et al.	Applicant
US7657224(B2)	2010-02-01	Goldberg	Applicant
US7657493(B2)	2010-02-01	Meijer	Applicant
US7680849(B2)	2010-03-01	Heller	Applicant
US7680959(B2)	2010-03-01	Svendsen	Applicant
US7685204(B2)	2010-03-01	Rogers	Applicant
US7693887(B2)	2010-04-01	McLaughlin	Applicant
US7707603(B2)	2010-04-01	Abanami	Applicant
US7734569(B2)	2010-06-01	Martin	Applicant
US7743009(B2)	2010-06-01	Hangartner	Applicant
US7797321(B2)	2010-09-01	Martin	Applicant
US7826444(B2)	2010-11-01	Irvin	Applicant
US7853712(B2)	2010-12-01	Amidon	Applicant
US7875788(B2)	2011-01-01	Benyamin	Applicant
US7889724(B2)	2011-02-01	Irvin	Applicant
US2001/0007099(A1)	2001-07-01	Rau	Applicant
US2001/0056434(A1)	2001-12-01	Kaplan	Applicant
US2002/0002899(A1)	2002-01-01	Gjerdingen	Applicant
US2002/0042912(A1)	2002-04-01	Iijima	Applicant
US2002/0059094(A1)	2002-05-01	Hosea	Applicant
US2002/0082901(A1)	2002-06-01	Dunning	Applicant
US2002/0152117(A1)	2002-10-01	Cristofalo	Applicant
US2002/0178223(A1)	2002-11-01	Bushkin	Applicant
US2002/0178276(A1)	2002-11-01	McCartney	Applicant
US2002/0194215(A1)	2002-12-01	Cantrell	Applicant
US2003/0022953(A1)	2003-01-01	Zampini et al.	Applicant
US2003/0033321(A1)	2003-02-01	Schrempp	Applicant
US2003/0097379(A1)	2003-05-01	Ireton	Applicant
US2003/0120630(A1)	2003-06-01	Tunkelang	Applicant
US2003/0212710(A1)	2003-11-01	Guy	Applicant
US2003/0221541(A1)	2003-12-01	Platt	Examiner
US2003/0229537(A1)	2003-12-01	Dunning	Applicant
US2004/0002993(A1)	2004-01-01	Toussaint	Applicant
US2004/0003392(A1)	2004-01-01	Trajkovic	Applicant
US2004/0068552(A1)	2004-04-01	Kotz	Applicant
US2004/0073924(A1)	2004-04-01	Pendakur	Applicant
US2004/0128286(A1)	2004-07-01	Yasushi et al.	Applicant
US2004/0139064(A1)	2004-07-01	Chevallier	Applicant
US2004/0148424(A1)	2004-07-01	Berkson	Applicant
US2004/0158860(A1)	2004-08-01	Crow	Applicant
US2004/0162738(A1)	2004-08-01	Sanders	Applicant
US2004/0194128(A1)	2004-09-01	McIntyre	Applicant
US2004/0267715(A1)	2004-12-01	Polson	Applicant
US2005/0021470(A1)	2005-01-01	Martin	Applicant
US2005/0060350(A1)	2005-03-01	Baum	Applicant
US2005/0075908(A1)	2005-04-01	Stevens	Applicant
US2005/0091146(A1)	2005-04-01	Levinson	Applicant
US2005/0102610(A1)	2005-05-01	Jie	Applicant
US2005/0114357(A1)	2005-05-01	Chengalvarayan	Applicant
US2005/0131752(A1)	2005-06-01	Gracie	Applicant
US2005/0141709(A1)	2005-06-01	Bratton	Applicant
US2005/0154608(A1)	2005-07-01	Paulson	Applicant
US2005/0160458(A1)	2005-07-01	Baumgartner	Applicant
US2005/0193014(A1)	2005-09-01	Prince	Applicant
US2005/0193054(A1)	2005-09-01	Wilson	Applicant
US2005/0195696(A1)	2005-09-01	Rekimoto	Applicant
US2005/0210009(A1)	2005-09-01	Tran	Applicant
US2005/0210101(A1)	2005-09-01	Janik	Applicant
US2005/0216859(A1)	2005-09-01	Paek	Applicant
US2005/0222989(A1)	2005-10-01	Haveliwala	Applicant
US2005/0223039(A1)	2005-10-01	Kim	Applicant
US2005/0234891(A1)	2005-10-01	Walther	Applicant
US2005/0235811(A1)	2005-10-01	Dukane	Applicant
US2005/0256867(A1)	2005-11-01	Walther	Applicant
US2005/0276570(A1)	2005-12-01	Reed, Jr.	Applicant
US2006/0015904(A1)	2006-01-01	Marcus	Applicant
US2006/0018208(A1)	2006-01-01	Nathan	Applicant
US2006/0018209(A1)	2006-01-01	Drakoulis et al.	Applicant
US2006/0020062(A1)	2006-01-01	Bloom	Applicant
US2006/0020662(A1)	2006-01-01	Robinson	Applicant
US2006/0026263(A1)	2006-02-01	Raghavan	Applicant
US2006/0062094(A1)	2006-03-01	Nathan	Applicant
US2006/0067296(A1)	2006-03-01	Bershad	Applicant
US2006/0074750(A1)	2006-04-01	Clark	Applicant
US2006/0080251(A1)	2006-04-01	Fried	Applicant
US2006/0080356(A1)	2006-04-01	Burges	Applicant
US2006/0091203(A1)	2006-05-01	Bakker	Applicant
US2006/0123052(A1)	2006-06-01	Robbin	Applicant
US2006/0136344(A1)	2006-06-01	Jones	Applicant
US2006/0143236(A1)	2006-06-01	Wu	Applicant
US2006/0156239(A1)	2006-07-01	Jobs et al.	Examiner
US2006/0165571(A1)	2006-07-01	Seon et al.	Applicant
US2006/0168340(A1)	2006-07-01	Heller et al.	Examiner
US2006/0168616(A1)	2006-07-01	Candelore	Applicant
US2006/0173916(A1)	2006-08-01	Verbeck Sibley	Applicant
US2006/0174000(A1)	2006-08-01	Abanami	Applicant
US2006/0195513(A1)	2006-08-01	Rogers	Applicant
US2006/0195514(A1)	2006-08-01	Rogers	Applicant
US2006/0195516(A1)	2006-08-01	Beaupre	Applicant
US2006/0195521(A1)	2006-08-01	New et al.	Applicant
US2006/0195581(A1)	2006-08-01	Vaman et al.	Applicant
US2006/0195789(A1)	2006-08-01	Rogers	Applicant
US2006/0195790(A1)	2006-08-01	Beaupre	Applicant
US2006/0206478(A1)	2006-09-01	Glaser	Applicant
US2006/0253874(A1)	2006-11-01	Stark	Applicant
US2006/0265421(A1)	2006-11-01	Ranasinghe et al.	Applicant
US2006/0277098(A1)	2006-12-01	Chung	Applicant
US2006/0282304(A1)	2006-12-01	Bedard et al.	Applicant
US2006/0282311(A1)	2006-12-01	Jiang	Applicant
US2006/0288367(A1)	2006-12-01	Swix	Applicant
US2007/0016507(A1)	2007-01-01	Tzara	Applicant
US2007/0043829(A1)	2007-02-01	Dua	Applicant
US2007/0073596(A1)	2007-03-01	Alexander	Applicant
US2007/0100690(A1)	2007-05-01	Hopkins	Applicant
US2007/0101373(A1)	2007-05-01	Bodlanender et al.	Applicant
US2007/0118546(A1)	2007-05-01	Acharya	Applicant
US2007/0136264(A1)	2007-06-01	Tran	Applicant
US2007/0156677(A1)	2007-07-01	Szabo	Applicant
US2007/0156732(A1)	2007-07-01	Surendran et al.	Applicant
US2007/0161402(A1)	2007-07-01	Ng	Applicant
US2007/0162546(A1)	2007-07-01	McLaughlin	Applicant
US2007/0203790(A1)	2007-08-01	Torrens	Applicant
US2007/0244880(A1)	2007-10-01	Martin	Applicant
US2007/0250429(A1)	2007-10-01	Walser	Applicant
US2007/0250761(A1)	2007-10-01	Bradley	Applicant
US2007/0265979(A1)	2007-11-01	Hangartner	Applicant
US2007/0271286(A1)	2007-11-01	Purang	Applicant
US2007/0271287(A1)	2007-11-01	Acharya et al.	Applicant
US2007/0294096(A1)	2007-12-01	Randall	Applicant
US2008/0004948(A1)	2008-01-01	Flake	Applicant
US2008/0004990(A1)	2008-01-01	Flake	Applicant
US2008/0021851(A1)	2008-01-01	Alcalde	Applicant
US2008/0027881(A1)	2008-01-01	Bisse	Applicant
US2008/0040326(A1)	2008-02-01	Chang et al.	Applicant
US2008/0046317(A1)	2008-02-01	Christianson	Applicant
US2008/0065659(A1)	2008-03-01	Watanabe et al.	Applicant
US2008/0077264(A1)	2008-03-01	Irvin	Applicant
US2008/0109378(A1)	2008-05-01	Papadimitriou	Applicant
US2008/0133601(A1)	2008-06-01	Cervera	Applicant
US2008/0154942(A1)	2008-06-01	Tsai et al.	Applicant
US2008/0155057(A1)	2008-06-01	Khedouri	Applicant
US2008/0155588(A1)	2008-06-01	Roberts	Applicant
US2008/0195438(A1)	2008-08-01	Manfredi	Applicant
US2008/0215173(A1)	2008-09-01	Hicken	Applicant
US2008/0220855(A1)	2008-09-01	Chen	Applicant
US2008/0256106(A1)	2008-10-01	Whitman	Applicant
US2008/0270221(A1)	2008-10-01	Clemens	Applicant
US2009/0006353(A1)	2009-01-01	Vignoli	Applicant
US2009/0024504(A1)	2009-01-01	Lerman	Applicant
US2009/0024510(A1)	2009-01-01	Chen	Applicant
US2009/0048957(A1)	2009-02-01	Celano	Applicant
US2009/0070267(A9)	2009-03-01	Hangartner	Applicant
US2009/0073174(A1)	2009-03-01	Berg	Applicant
US2009/0076939(A1)	2009-03-01	Berg	Applicant
US2009/0076974(A1)	2009-03-01	Berg	Applicant
US2009/0083307(A1)	2009-03-01	Martin Cervera et al.	Applicant
US2009/0089222(A1)	2009-04-01	Ferreira De Castro et al.	Applicant
US2009/0106085(A1)	2009-04-01	Raimbeault	Applicant
US2009/0164641(A1)	2009-06-01	Rogers	Applicant
US2009/0210415(A1)	2009-08-01	Martin	Applicant
US2009/0276368(A1)	2009-11-01	Martin	Applicant
US2010/0161595(A1)	2010-06-01	Martin et al.	Applicant
US2010/0169328(A1)	2010-07-01	Hangartner	Applicant
US2011/0119127(A1)	2011-05-01	Hangartner	Applicant
EP1050833(A2)	2000-08-01		Applicant
EP1231788(A1)	2002-08-01		Applicant
EP1420388(A1)	2004-04-01		Applicant
EP1548741(A1)	2005-06-01		Applicant
JP11-052965	1999-02-01		Applicant
JP2002-108351(A)	2002-04-01		Applicant
JP2002-320203(A)	2002-10-01		Applicant
JP2003-022958(A)	2003-09-01		Applicant
JP2003255958	2003-09-01		Applicant
JP2004-221999(A)	2004-08-01		Applicant
JP2005/027337(A)	2005-01-01		Applicant
KR2002025579	2002-04-01		Applicant
WO3/036541(A1)	2003-05-01		Applicant
WO3/051051(A1)	2003-06-01		Applicant
WO2004/070538(A2)	2004-08-01		Applicant
WO2005/013114(A1)	2005-02-01		Applicant
WO2005/115107(A2)	2005-12-01		Applicant
WO2006/052837(A2)	2006-05-01		Applicant
WO2006/075032	2006-07-01		Applicant
WO2006034218	2006-08-01		Applicant
WO2006/114451	2006-11-01		Applicant
WO2007/038806(A3)	2007-04-01		Applicant
WO2007/134193(A3)	2007-05-01		Applicant
WO2007/075622(A2)	2007-07-01		Applicant
WO2007/092053(A1)	2007-08-01		Applicant
WO2009/149046(A1)	2009-12-01		Applicant

Non-Patent Literature (96)

Chao-Ming et al. (Chao-Ming), Design and Evaluation and mProducer: a Mobile Authoring Tool for Personal Experience Computing [online], MUM 2004, College Park, Maryland, USA, Oct. 27-29, 2004 [retrieved on Dec. 17, 2010]. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.131.2933&rep=rep1&type=pdf].Applicant
Dempster, Y., Laird, N., and Rubin, D. “Maximum Likelihood from Incomplete Data via the EM Algorithm”. Jour. of the Royal Stat. Soc., Ser. B., 39:1047-1053, 1977.Applicant
Bender, et al., “Newspace: Mass Media and Personal Computing,” Proceedings of USENIX, Conference, pp. 329-348 (Summer 1991).Applicant
Jon Orwant, “Appraising the User of User Models: Doppelgänger's Interface,” in: A. Kobsa and D. Litman (eds.), Proceeding of the 4th International Conference on User Modeling (1994).Applicant
Lazar, N.A.; Bayesian Empirical Likelihood; Technical Report, Carnegi Mellon University, Department of Statistics, 2000; 26 pages.Applicant
Hofmann, T. “Unsupervised Learning by Probabilistic Latent Semantic Analysis”. Mach. Learn., 42:177-196, 2001.Applicant
PolyLens: A Recommender System for Groups of Users; M. O'Connor, D. Cosley, J.A. Konstan, J. Riedl; European Conference on Computer Supported Co-Operative Work at Bonn, Germany; Published 2001; pp. 199-218.Applicant
Platt, John C. et al., “Learning a Gaussian Process Prior for Automatically Generating Music Playlists,” Microsoft Corporation {platt, cburgess, sswenson, chriswea}@microsoft.com, [email protected], 2002; pp. 1-9.Applicant
Hofmann, T. “Latent Semantic Models for Collaborative Filtering”. ACM Transactions on Information Systems, 22:89-115, 2004.Applicant
Indyk, P. and Matousek, J. “Low-Distortion Embeddings of Finite Metric Spaces”. In Handbook of Discrete and Computational Geometry, pp. 177-196. CRC Press, 2004.Applicant
Wolfers, Justin and Zitzewitz, Eric, Prediction Markets, Journal of Economic Perspectives, Spring 2004, pp. 107-126, vol. 18, No. 2.Applicant
Platt, John S., “Fasting Embedding of Sparse Music Similarity Graphs,” Microsoft Corporation, {[email protected]}; 2004.Applicant
Toward alernative metrics of journal impact: a comparison of download and citation data, Johan Bollen, Herbert Van de Sompel, Joan Smith, Rick Luce, Google.com, 2005, pp. 1-2.Applicant
Das,A., Datar,M., Garg,A., and Rajaram,S. “Google News Personalization: Scalable Online Collaborative Filtering”. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pp. 271-280, New York, NY, USA, 2007. ACM Press.Applicant
Scihira, I. “A Characterization of Singular Graphs”. Electronic Journal of Linear Algebra, 16:451-462, 2007.Applicant
Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., and Aly, M. “Video Suggestion and Discovery for YouTube: Taking Random Walks Through the View Graph”. In WWW '08: Proceedings of the 17th international conference on World Wide Web, pp. 895-904, Beijing, China, 2008. ACM Press.Applicant
Dean, J. and Ghemawat, S. “MapReduce: Simplied Data Processing on Large Clusters”. Commun. ACM, 51(1):107-113, 2008.Applicant
John Thompson, “A Graphic Representation of Interaction With the NEXIS News Database,” MIT Thesis (May 1983).Applicant
Lippman, et al., “News and Movies in the 50 Megabit Living Room,” IEEE/IEICE, Global Telecommunications Conference, pp. 1976-1981 (Nov. 15, 1987).Applicant
Lie, “The Electronic Broadsheet—All The News That Fits the Display,” MIT Master's Thesis, pp. 1-96 (Jun. 1991).Applicant
Jonathan L. Orwant, “Doppelgänger: A User Modeling System,” MIT Bachelor's Thesis (Jun. 1991).Applicant
“Lessons from LyricTimeTM: A Prototype Multimedia System” 4th IEEE ComSoc International Workshop on Multimedia Communications (Apr. 1992).Applicant
Delivering Interactive Multimedia Documents over Networks; Shoshana Loeb; IEEE Communications Magazine; May 1992; 8 pages.Applicant
“Communications of the ACM” Dec. 1992, vol. 35, No. 12 at pp. 26-28 (Introduction to special issue regarding Workshop on High Performance Information Filtering, Morristown, N.J. Nov. 1991).Applicant
Belkins, et al., “Information Filtering and Information Retrieval: Two Sides of the Same Coin?”, Communications of the ACM (Dec. 1992).Applicant
Architecting Personalized Delivery of Multimedia Information,: Communications of the ACM (Dec. 1992).Applicant
Jonathan L. Orwant, “Doppelgänger Goes to School: Machine Learning for User Modeling,” MIT Master of Science Thesis (Sep. 1993).Applicant
Pachet, Francois, A Taxonomy of Musical Genres, Content-Based Multimedia Information Access Conference (RIAO), Paris, Apr. 2000, 8 pages.Applicant
Smart Computing, “The Scoop on File-Sharing Services,” Dec. 2000, vol. 11, Issue 12; pp. 30-33 in printed issue. Available at www.smartcomputing.com/editorial/article.asp?article=articles%2F2000%Fs1112%2F08s12.asp.Applicant
Carlson et al. “Internet Banking Market Developments and Regulatory Issues in the New Economy: What Changed, and the Challenges for Economic Policy . . . ”; May 2001; http://www.occ.gov/netbank/SGEC2000.pdf.Applicant
Bender, “Twenty Years of Personalization: All about the Daily Me,” Educause Review (Sep./Oct. 2002).Applicant
Tom Bunzel, “Easy Digital Music,” QUE Publisher, Aug. 18, 2004, Chapters 5 and 8.Applicant
The Trustees of Indiana University, Variations2, The Indiana University Digital Music Library, http://dml.indiana.edu/, last updated May 11, 2005, 1 page.Applicant
PCT/ES2005/00003 Written Opinion of the International Searching Authority Report dated Jun. 10, 2005.Applicant
“New Music Recommendation System is Based on FOAF Personal Profiling,” www.masternewmedia.org/music—recommendation/music—recommendation—system—FOAF, Oct. 1, 2005.Applicant
Co-Construction of Hybrid Spaces; Asa Rudstrom; A Dissertation submitted to the University of Stockholm in partial fulfillment of the requirements for the Degree of Doctor of Philosophy; Department of Computer and Systems Sciences Stockholm University and Royal Institute of Technology; pp. 1-69; Nov. 2005.Applicant
MobiLenin—Combining A Multi-Track Music Video, Personal Mobile Phones and a Public Display into Multi-User Interactive Entertainment; Jurgen Scheible, et al. Media Lab, University of Art and Design, Helsinki, Finland; pp. 1-10; Nov. 6-10, 2005.Applicant
PCT/ES2005/000213 Written Opinion of the International Searching Authority dated Jan. 12, 2006.Applicant
PCT/US2006/003795; International Search Report and Written Opinion of International Application, dated May 28, 2008.Applicant
“Social Networking Meets Music Listening: Mecora Launches Radio 2.0,” www.masternewmedia.org/news/2006/04/13/social—networking—meets—music—listening.htm, Apr. 13, 2006.Applicant
www.rfidjournal.com/article/articleview/1619/1/1, Web Page, RFID brings messages to Seattle side walks on RFID system being deployed next week will send marketing and assistive information to users carrying active RFID tags. RFID Journal (pp. 1-4).Applicant
Alvear, Jose, “Risk-Free Trial Streaming Media Delivery Tools,” Streaming Media.com; www.streamingmedia.com/article.ap?id=5768, Jun. 30, 2000.Applicant
www.akoo.com/Akoo/, Web Page, AKOO, Pick the Music, Waiting in the line at the Theme Park, Introducing the m-Venue™ platform.Applicant
www.alwayson-network.com/comments.php?id=P12663 0 37 0 C, Web Page, Not Your Average Jukebox, On Hollywood 1000 contender Ecast uses broadbank to being the digital media experience to your watering hole.Applicant
www.axcessnews.com/modules/wfsection/article.php?articleid=8327, Web Page, Feb. 24, 2006, Maintenance Fees, Digital Music Sales Triple to $1.1 Billion in 2005.Applicant
www.bmi.com/news/200403/20040324b.asp, Web Page, BMI™ Figures Don't Lie, Mar. 24, 2004, Touch Tunes Signs License Agreement for BMI Music in Digital Jukeboxes.Applicant
www.ecastinc.com/music—licensing.html, Web Page, ECAST Network, interactive entertainment network, Music/Licensing.Applicant
www.roweinternational.com/jukeboxes—dia.html, Web Page, Digital Internet Access Jukeboxes, Rowe International.Applicant
www.touchtunes.com, Web Page, Touchtunes, Turn your ROWE 100A's and 100B's into touch tunes Digital Jukeboxes—Bose.Applicant
PCT/US2006/048330; International Bureau; PCT Search Report and Written Opinion; Mar. 20, 2008; 10 pages.Applicant
IP City, Integrated Project on Interaction and Presence on Urban Environments-Demonstrators on Large-Scale Events Applications; ipcity.eu; Giulio Jacucci, John Evans, Tommi Ilmonen; pp. 1-37; Feb. 9, 2007.Applicant
PCT/US2006/034218; USPTO Search Authority; PCT International Search Report; Feb. 9, 2007.Applicant
PCT/ES2005/00003 Written Opinion of the International Preliminary Examining Authority dated Mar. 19, 2007.Applicant
PCT/US2007/068708; International Search Report; May 10, 2007.Applicant
Apple: iTunes 4.2 User Guide for Windows; Dec. 2003; retrieved from the internet: URL: http://www2.austin.cc.tx.us/tcm/projects/itunes.pdf; pp. 10, 17-19.Applicant
Deshpande, Mukund, et al., “Item-Based Top-N Recommendation Algoriths,” ACM Transactions on Information Systems, 22:1 (Jan. 2004), pp. 143-177.Applicant
Augmenting the Social Space of an Academic Conference; McCarthy, et al. Information School, University of Washington and Department of Computer Science and Engineering, University of Minnesota; pp. 1-10; Nov. 6-10, 2004.Applicant
Toward University Mobile Interaction for Shared Displays; Tim Paek, et al.; Microsoft Research, Redmond, WA; pp. 1-4; Nov. 6-10, 2004.Applicant
PCT/ES2005/00003 International Preliminary Report on Patentability (Ch II) Report dated May 22, 2007.Applicant
PCT/ES2005/000213 International Preliminary Report on Patentability (Ch II) Report Dated Nov. 15, 2007.Applicant
Strands Business Solutions. “Integration Document v.2.0”; Published May 2008; [online retrieved on Jan. 21, 2010] Retrieved from the internet <URL: http://recommender.strands.com/doc/SBS-Integration-Document.pdf>; entire document-18 pages.Applicant
Incremental tensor analysis: theory and applications, Jimeng Sun, Dacheng Tao, Spiros Papadimitriou, Philip Yu, Christos Faloutsos, ACM, Oct. 2008, pp. 1-37.Applicant
ShopSmart: Product Recommendations through Technical Specifications and User Reviews; Alexander Yates et al. Temple University; CIKM; Oct. 26-30, 2008, Napa Valley, CA, USA; 2 pages.Applicant
PCT/US07/068708; Filed May 10, 2007; International Search Report and Written Opinion; WO 2007/134193; Dec. 7, 2007.Applicant
PCT/US09/42002; Filed Apr. 28, 2009; International Search Report and Written Opinion; Jun. 2009.Applicant
PCT/US09/45911; Filed Jun. 2, 2009; International Search Report and Written Opinion.Applicant
Industry Standard, The, Help FAQs for Standard Prediction Market, http://www.thestandard.com/help, downloaded Jun. 29, 2009.Applicant
PCT/US2007/09/45725; International Search Report—WO; Jul. 15,2009.Applicant
International Search Report PCT/US2009/051233; Sep. 4, 2009; Strands, Inc.Applicant
PCT/US2006/004257 European Search Report Oct. 23, 2009.Applicant
IEEE, no matched results, Nov. 11, 2009, 1 page.Applicant
PCT/US09/68604 International Search Report and Written Opinion of the International Searching Authority; dated Feb. 17, 2010.Applicant
Canada—Examiner's Report for App. No. 2713507 mailed on Sep. 21, 2011.Applicant
UK Search Examination Report, App. No. GB1014954.0, mailed Dec. 16, 2010.Applicant
Yen, Yi-Wyn, Apple announces a 32GB iPhone 3G by Jun. 15, 2009, The Industry Standard, Apr. 2, 2009, http://www.thestandard.com/preditions/channel/hardware, downloaded Apr. 8, 2009.Applicant
Treemap, University of Maryland, http://www.cs.umd.edu/hcil/treemap/ , last updated Aug. 5, 2003, 4 pages., Aug. 5, 2003.Applicant
“Communication from the Examining Division dated Jul. 22, 2011”, European Patent Application No. 09169573.4 (8 pages).Applicant
“Final Office Action dated Jan. 1, 2012”, U.S. Appl. No. 12/242,728 (15 pages).Applicant
“Final Office Action dated Nov. 10, 2011”, U.S. Appl. No. 12/242,768 (10 pages).Applicant
“Non-Final Office Action dated Feb. 16, 2012”, U.S. Appl. No. 12/242,758.Applicant
“Non-Final Office Action dated Feb. 9, 2012”, U.S. Appl. No. 12/646,916 (13 pages).Applicant
“Non-Final Office Action dated Jan. 9, 2012”, U.S. Appl. No. 12/242,735 (12 pages).Applicant
Cano, Pedro et al., “On the Use of FastMap for Audio Retrieval and Browsing”, Cano, Pedro et al., On the Use of FastMap for Audio Retrieval and Browsing, The International Conference on Music Information Retrieval and Related Activities (ISMIR 2002), Paris, France, Oct. 2002,2 pages., 2002.Applicant
Connell, Lain et al., “Ontological Sketch Models: Highlighting User-System Misfits”, Connell, lain et al., Ontological Sketch Models: Highlighting User-System Misfits, In P. Palanque, E. O'Neill and P. Johnson, editors, Proceedings of Human Computer Interaction (HCI) Bath, England, Sep. 2003, London Springer, pp. 1-16., Sep. 2003, 1-16.Applicant
Logan, Beth , “A Music Similarity Function Based on Signal Analysis”, Logan, Beth et al., A Music Similarity Function Based on Signal Analysis, IEEE International Conference on Multimedia and Expo (ICME), Tokyo, Japan, Aug. 2001, IEEE Press, pp. 952-955., Aug. 2001, 952-955.Applicant
Logan, Beth , “Content-Based Playlist Generation: Exploratory Experiments”, Logan, Beth, Content-Based Playlist Generation: Exploratory Experiments, The International Conference on Music Information Retrieval and Related Activities (ISMIR 2002), Paris, France, Oct. 2002,2 pages., Oct. 2002.Applicant
Maidin, Donncha et al., “The Best of Two Worlds: Retrieving and Browsing”, Maidin, Donncha 0 et al., The Best of Two Worlds: Retrieving and Browsing, Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-OO), Verona, Italy, Dec. 7-9, 2000,4 pages., Dec. 2000.Applicant
Australia Examiner's 1st Report on patent application No. 2010212503 mailed Jun. 27, 2011.Applicant
“PCT/US 10/47955 International Search Report”, PCT/US 10/47955 International Search Report mailed Nov. 8, 2010.Applicant
Notess, Mark et al., Notess, Mark et al., Variations2: Toward Visual Interface for Digital Music Libraries, Second International Workshop on Visual Interfaces to Digital Libraries, 2002, 6 pages., 2002.Applicant
Pampalk, Elias et al., “Content-based Organization and Visualization of Music Archives”, Pampalk, Elias et al., Content-based Organization and Visualization of Music Archives, ACM Multimedia, Juan les Pins, France, Dec. 2002, pp. 570-579., Dec. 2002, 570-579.Applicant
Pauws, Steffen et al., “PATS: Realization and User Evaluation of an Automatic Playlist Generator”, Pauws, Steffen et al., PATS: Realization and User Evaluation of an Automatic Playlist Generator, The International Conferences on Music Information Retrieval and Related Activities (ISMIR 2002), Paris, France, Oct. 2002,9 pages., Oct. 2002.Applicant
Rauber, Andreas et al., “The SOM-enhanced JukeBox: Organization and visualization of Music Collections Based on Perceptual Models”, Rauber, Andreas et al., The SOM-enhanced JukeBox: Organization and Visualization of Music Collections Based on Perceptual Models, Journal of New Music Research, vol. 32, Nov. 2, 2003, pp. 193-210., Nov. 2, 2003, 193-210.Applicant
Shneiderman, Ben , “Tree Visualization with Tree-Maps: 2-d Space-Filling Approach”, Shneiderman, Ben, Tree Visualization with Tree-Maps: 2-d Space-Filling Approach, ACM Transactions on Graphics, vol. 11, No. 1, Jan. 1992, pp. 92-99., 1992, pp. 92-99.Applicant
Shneiderman, Ben , “Treemaps for Space-Contrained Visualization of Hierarchies”, Shneiderman, Ben, Treemaps for Space-Contrained Visualization of Hierarchies, http://www.sc.umd.edu/heil/treemap- history, last updated Apr. 28, 2006, 16 pages., Apr. 28, 2006.Applicant
Tzanetakis, George et al., “A Prototype Audio Browser-Editor Using a Large Scale Immersive Visual and Audio Display”, Tzanetakis, George et al., MARSYAS3D: A Prototype Audio Browser-Editor Using a Large Scale Immersive Visual and Audio Display, Proceedings of the 2001 International Conference on Auditory Display, Espoo, Finland, Jul./Aug. 2001, 5 pages., 2001.Applicant

Abstract

Description

RELATED APPLICATIONS

COPYRIGHT NOTICE

TECHNICAL FIELD

BACKGROUND OF THE INVENTION

BRIEF SUMMARY OF PRESENTLY PREFERRED EMBODIMENTS

[0015] Various different metrics between and among media items can be generated from the knowledge base of mediasets. Such metrics can include but are not limited to the follow examples:

- [0016] a) Pre-concurrency (for ordered mediasets) between two items is computed as the number of times a given item precedes the other item in the mediasets of the knowledge base.
- [0017] b) Post-concurrency (for ordered mediasets) between two items is computed as the number of times an item follows another item in the mediasets of the knowledge base.
- [0018] c) Co-concurrency between two items is computed as the number of times the items appear together in a mediaset.
- [0019] d) Metadata similarities may be computed as well by considering keywords associated with the media items such as artist, actor, date, etc.
- [0020] e) Combinations of the previous metrics can be useful.
- [0021] f) Combinations of the previous metrics applying transitivity.

- [0023] a) Filters that the user expresses to focus the recommendation only on a determined type of items.
- [0024] b) Filters that the user expresses to focus the recommendations on items that meet certain keyword-based criteria, such as a specific artist's, year/s, genre/s, etc.
- [0025] c) Filters that personalize the recommendations to the user. This kind of filtering includes recommending only items that the user knows about, or only items that the user does not know about, etc.