Automated content and collaboration-based system and methods for determining and providing content recommendations

What is claimed is:

1. A content item referral system, executable by a computer, providing for the automated presentation of a set of recommended media-based content items in response to a query presented by a user, said content item referral system comprising: a) a weighted relation subsystem operable to provide weighted relationships data representing relative similarities between characteristic attributes of a predetermined set of content items; b) a referral sub-system, coupled to receive user profile data and said weighted relationship data, responsive to a user query, said referral system operative to perform a traversal of said user profile data and said weighted relationship data to provide an ordered list of content items relative to a predetermined content item; and c) an action analysis sub-system coupled to said referral system to receive user action behaviors correlated to content items considered by said user, said action analysis sub-system providing said user profile data to said referral sub-system.

2. The content referral system of claim 1 wherein a user profile sub-system stores said user profile data in a form reflecting weighted relationships representing relative similarities between characteristic attributes of a user considered set of content items and wherein said referral sub-system is operable to combine said user profile data with said weighted relationship data.

3. The content referral system of claim 2 wherein said referral sub-system is operable as a graph traversal system over said combined user profile data and said weighted relationship data.

4. The content referral system of claim 3 wherein said user considered set of content items is a subset of said predetermined set of content items.

5. A method of providing media content recommendations through a computer server system connected to a network communications system, wherein said computer server system has access to a first database of media content items including media content and related information and a media content filter identifying and providing qualifying attribute relationship data for media content items within said first database, and wherein the media content recommendations are particularly tailored to the personalized interests of a user, said method comprising the steps of: a) presenting media content items through a network-connected interface to a predetermined user for review and consideration of potential personal interest; b) monitoring the consideration of said media content items implied through the user directed navigation among the presented media content items and user requests for related information; c) collecting data from said step of monitoring to develop a user weighted data set reflective of said predetermined user's relative consideration of said media content items; and d) evaluating said user weighted data set in combination with said media content filter to identify a set of media content items accessible from said first database for presentation to said predetermined user consistent with said step of presenting.

6. The method of claim 5 wherein said step of collecting data further provides for the progressive refinement of said user weighted data set, whereby said step of evaluating progressively provides said set of media content items more closely tailored to the personalized interests of said predetermined user.

7. The method of claim 6 wherein said step of collecting data includes identifying the occurrence of predetermined actions taken by said predetermined user and determining selectively the durations of said predetermined actions relative to different media content items, and wherein said predetermined actions taken and the durations of said predetermined actions are incorporated into said user data set to reflect said predetermined user's relative consideration of said media content items.

8. The method of claim 7 wherein said step of evaluating utilizes said user weighted data set to extrapolate through the relationships identified through said media content filter to identify said set of media content items.

9. The method of claim 8 wherein said step of evaluating operates as a graph traversal over the media content items related through said media content filter where the weighting data of said media content filter are modified to reflect said user weighted data set.

10. A content referral server system supporting, via a communications network, remote access, by a client system, to information relating, based on the similarity of characteristic attributes of specific instances of such content, different content items served by said content referral server system, said content referral server system comprising: a) a content relations system that provides access to weighted content relationship information defining similarities between characteristic attributes of content referenceable by said content relations system; and b) a profiling system that collects profiling information reflecting the navigational actions of a user of said client system in accessing said content referral server system, wherein said profiling system provides profile data combinable with said weighted content relationship information relative to content referenceable by said content relations system and selectable by said user of said client system.

11. The content referral server system of claim 10 wherein said profiling system provides profile data combinable with said weighted content relationship information relative to a characteristic attribute of the content selectable by said user of said client system.

12. The content referral server system of claim 11 wherein said profiling information reflects explicit indications of interest and implicit indications of interest in particular instances of content.

13. The content referral server system of claim 12 wherein said implicit indications of interest are derived from the selection and duration of sampling of particular instances of content.

14. A content item referral system, executable by a computer, providing for the automated presentation of a set of recommended media content items in response to a query presented by a user, said content item referral system comprising: a) a first database storing weighted relationships data representing relative similarities between characteristic attributes of a predetermined set of content items; b) a second database storing user profile data including weighted preferences with respect to a profile respective set of content items; c) a referral generation system, coupleable to said first database and said second database to access said weighted relationship data and said user profile data, wherein said referral generation system is responsive to a user query to define a graph traversal of said weighted relationship data combined with said user profile data and qualified by a weighted rating and confidence level at predetermined graph traversal steps to provide an ordered list of content items responsive to sold user query and having a predetermined minimum weighted rating and confidence level.

15. The content item referral system of claim 14 wherein said content item referral system is interactively responsive to said user to receive said user query and user input representing user action behaviors and wherein said content item referral system further comprises a profile update system coupled to said second database to update user profile data corresponding to said user in response to user action behaviors correlated to content items considered by said user.

16. The content item referral system of claim 15 wherein said user action behaviors provide predetermined attributed data correlated to content items considered by said user, wherein an update weighted preference is determinable from said predetermined attributed data, and wherein said profile update system updates said second database with said updated weighted preference.

17. The content item referral system of claim 16 wherein said user action behaviors include navigational and content item selection actions.

18. The content item referral system of claim 17 wherein said attributed data defines an implicit level of interest by said user in a predetermined content item.

19. A content item referral system executable by a server computer system coupleable to a communications network and interactively responsive to user actions in connection with the review and selection of media content items through distributed client computer systems, including the sampling of media content items, to provide automated generation of recommended sets of media content items, said content item referral system comprising: a) an expert database containing attributed expert weighting data defining a first node connected network describing a defined set of content items; b) a user profile database containing a plurality of user profiles, wherein each said user profile includes attributed personalized weighting data for a respective subset of said defined set of content items, wherein said attributed personalized weighting data is derived from explicit and implicit user actions correlated to said plurality of user profiles, and wherein said attributed personalized weighing data defines a second node connected network; c) a referral system, coupled to said expert database and said user profile database, responsive to a user query to provide a list of recommended media content items to evaluate a plurality of traversal paths through said first and second node connected networks qualified by a combination of said attributed expert weighting data and said attributed personalized weighting data having at least a determined minimum aggregate weighting, the terminal nodes of said traversal paths representing said list of recommended media content items.

20. The content item referral system of claim 19 wherein said referral system is responsive to group behavioral data reflecting explicit and implicit user actions correlated to content items within said set of content items.

21. The content item referral system of claim 20 further comprising a weighting filter coupled between said expert database and said referral system, said weighting filter providing for the normalization of said attributed expert weighting data with respect to said attributed personalized weighting data.

22. The content item referral system of claim 21 wherein the normalization provided by said weighting filter is derived from said group behavioral data.

Description Submit all comments and votes

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is generally related to the collection, processing, and presentation of alternative information source content to a user and, in particular, the selective and automated generation of source content alternatives based on content relationships and user behavioral patterns to support the recommendation of alternative content sources.

2. Description of the Related Art

There are an increasing number of typically entertainment oriented media items, such as music, books, videos, and other content sources, available for purchase by users. A currently existing system, available to at least some users, is capable of presenting the details of over 300,000 individual music compact discs alone for purchase by a user. The collection of source content is growing with the continual addition of new content titles as well as the development and adoption of new content technologies, such as MP3, digital music software. Thus, a potential purchaser faces a significant investment of time and expense to comfortably select an appropriate item for purchase.

Existing source content selection systems are quite ineffective in supporting content searches much beyond using artist, collection, and title. Users therefore typically confine their searches to just those media items that are independently known to them or are aware of through other sources of media information. These other sources are typically sufficient to provide indications of whether and which segments of the general population might appreciate particular content items. No indication is given and none can be reliably inferred as to whether a particular user will enjoy or appreciate a given item.

There is, at least for entertainment media content, some acceptance of the belief that a user's appreciation of particular content items can suggest the user's likely appreciation of other content titles. Systems built to exploit this belief have met with limited results. One known system, apparently a neural-net based expert system, determines and provides recommendation of other content titles based purely on the similarities between users without considering the relationships between the music items from a content or contextual point of view. These systems have the disadvantage that they require an initial "teaching" period where the recommendations given to users are likely to be inaccurate. Another disadvantage is that the user does not understand the reasoning behind the recommendations and therefore does not trust the recommendations. The absence of confidence in whatever recommendations are given directly reduces the utility of the system. Additionally, such systems tend to generate recommendations that reflect the lowest common denominator between broad users tastes. As a result, these systems typically provide recommendations reflecting potential appreciation within a single generic style, such as only 1980's pop music. These systems do not appear to be effectively capable of providing recommendations across a diverse range of music, such as Death Metal and Classical.

Another known system recommends particular content items based on the given content or style of the item. Such systems are generally established by hand, requiring a broad, yet detailed, understanding of each media item. Establishing even basic knowledge-based systems requires a substantial investment in time and other costs. Therefore, these systems typically employ simplistic relationships between items, such as broad categories, such as Drama and Comedy, for relating content. Since these categories contain large numbers of content items, any user selection against the categories is likely to return an also large set of recommendations and, therefore, is unlikely to be significantly useful to a user.

Finally, both of these existing systems produce recommendations that are effectively final end-points in the recommendation search. No clear ability is provided for users to explore further items related to the recommendations. Thus, the user is often left with recommendations, which are almost correct, but which don't raise the user's propensity to consume to the level required to purchase/consume the content.

SUMMARY OF THE INVENTION

Therefore, a general purpose of the present invention to provide a system that combines content-based filtering and progressively refined collaborative-based filtering to deliver a set of media item recommendations that are consistent with a user's personal media content interests.

This purpose is achieved in the present invention by providing a system and method of providing media content recommendations through a computer server system connected to a network communications system. The computer server system preferably has access to a first database of media content items including media content and related information and a media content filter identifying and providing qualifying attribute relationship data for media content items within the first database. The media content recommendations are particularly tailored to the personalized interests of a user through sequence of steps including presenting media content items through a network-connected interface to the user for review and consideration of potential personal interest, monitoring the consideration of the media content items implied through the user directed navigation among the presented media content items and user requests for related information; collecting the monitored data to develop a user weighted data set reflective of the user's relative consideration of the media content items; and evaluating the user weighted data set in combination with the media content filter to identify a set of media content items accessible from the first database for re-presentation to the user.

Thus, the operation of the present system reflects the consideration that media content items, such as music, video, and other forms of content, can be interrelated based on multiple characterizing attributes. The strength of these characterizing attributes, or similarities, is used to further define these content-based relationships, even as between quite different forms or types of media content. An additional aspect of the operation of the present invention allows for the progressive or continuing collaborative, including self-collaborative, development of such content-based relationships.

An advantage of the present invention, therefore, is that the provided combination of content and collaborative recommendation systems enables the delivery of recommendations that are particularly tailored to the personalized interests of a user.

Another advantage of the present invention is that the system flexibly determines a scope of applicable similarities between a particular and other users and recommends items within the applicable scope.

A further advantage of the present invention is that the self-collaborative relationships developed for individual users of the system permit the development of individualized recommendations even where the group collaborative relationships reflect the choices of users with highly diverse media content interests.

Still another advantage of the present invention is that the system enables multi-level media content relationship information to be captured and used as data evaluateable in providing particularized media content item recommendations.

Yet another advantage of the present invention is that implicit and explicit collaborative data is captured from and in consideration of particular users, supporting both the continuing development of both group and personal interest profiles. The implicit collaborative data is advantageously obtained from a user's self-directed actions of reviewing and considering different media content items. Thus, the selection of items to review and the length and nature of the consideration of such items inferentially reflects the user's relative interest in particular media content items. Confidence levels in the inferences drawn can also be developed and refined through the continued monitoring of user actions in reviewing and considering the same and closely similar media content items. The explicit information provided by users regarding the level and nature of their interest in different media content items provides high-confidence information that can be incorporated into the group and individualized collaborative data.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other advantages and features of the present invention will become better understood upon consideration of the following detailed description of the invention when considered in connection with the accompanying drawings, in which like reference numerals designate like parts throughout the figures thereof, and wherein:

FIG. 1A provides an overview of the logical hardware and system implementation, including navigational user interface, of a preferred embodiment of the present invention;

FIG. 1B provides a process overview of a preferred embodiment of the present invention;

FIG. 2 is a detailed block diagram detailing the system operation of the personalized referral system implemented in accordance with a preferred embodiment of the present invention;

FIG. 3 provides a block diagram detailing the collection and compilation of behavioral data in accordance with a preferred embodiment of the present invention;

FIG. 4 illustrates the collection and correlation of information gathered from multiple information sources as may be utilized to establish profiles of individualized and group behaviors as a basis for determining and providing recommendation sets in accordance with a preferred embodiment of the present invention;

FIG. 5 provides a representation of corresponding portions of individualized user profile data sets reflecting the strength and confidence of relationships between particular media content items and users;

FIG. 6 is a graph representation of the media content characterization attribute network utilized in accordance with a preferred embodiment of the present invention to develop individualized media content item recommendations; and

FIGS. 7a, 7b, 7c, and 7D provide flowcharts of a preferred system operation, detailing the collection and processing of user input and the presentation of resulting recommendations back to the user.

DETAILED DESCRIPTION OF THE INVENTION

The present invention operates to provide users with a source of recommendations for different media content items that may then be purchased or otherwise acquired by a user. These media content items are broadly any potentially consumable unit of content that can be characterized by content attributes. The content may be presented, sampled, used, and consumed in any of an open set of presentation formats, including audio and visual works, streaming and static pictorial images and clips, documents and reference materials alone or associated with other content. In the exemplary case of audio content, media content items may be music samples, song tracks, and albums and CDs, which may also be referred to as collections. Music videos, cover art, and liner notes may be treated as independent media content items separately consumable or as components of song tracks and collections as may be appropriate.

As illustrated in FIG. 1A, a preferred embodiment 10 of the present invention provides for the development of media content item recommendations within the scope of a transaction performed over a communications network, such as the Internet. The system and methods of the present invention preferably provide for a user, operating a user computer system 12 with a network access supported interface 14, such as a conventional Web browser application, to access and navigate, via a communications network 16, through information presented by a server computer system 18. Preferably, the Web browser 14 operated by the user includes or is augmented with plug-ins and applications supporting the presentation of streaming audio and video data as may be returned from the server computer system 18 to the user computer system 12.

Recommendation and navigational requests are presented effectively by the user to a referral system 20 within the server system 18. Explicit profiling data provided by the user and implicitly derived from referral system 20 processes are preferably processed 22 and stored 24 by the server system 18. This explicit and particularly the implicit profiling data gathered is then used to provide individualizing recommendations for particular users. The profiling data collected from individuals is also preferably combined to form a collaboratively developed basis for modifying and expanding on the individualized recommendations that might be otherwise produced by the referral system 20.

In the currently preferred embodiment of the present invention, an expert compiled database 26 of content item relationship information is used as another basis for generating media content item recommendations. This database 26 preferably specifies logical connections between different media content items based on the sharing or similarity of characterizing attributes. In the case of music-type audio media content, these characterizing attributes maybe recognized as the empirically defined genre distinctions that occur between different music content items. These distinctions may be identified as belonging within some generic categories or styles, such as orchestral, blues, and pop, and perhaps within somewhat more descriptive categories, such as 1980s Dance, Rock Anthems, and Techno-Ambient Synth Mixes. The level of distinction utilized in connection with the present invention is empirically determinable, based largely on the availability of detailed relationship characterization data and the processing power and throughput restrictions of the server computer system 18.

The content item relationship database 26 preferably stores relative weighting factors that serve to establish the strength of the relationships identified in the database 26 between different media content items. These weightings, along with the establishment of the different distinguishable characterizing attributes are also preferably compiled by experts or expert systems. A preferred database 26 suitable for use with the present invention may be obtained commercially from All Media Guide, 301 East Liberty, Suite 400, Ann Arbor, Mich. 48104, a subsidiary of Alliance Entertainment Corporation, 4250 Coral Ridge Drive, Coral Springs, Fla. 33065.

The referral system 20 thus operates from a user provided request, typically identifying some media content item or artist, individual and collaborative profiles 24, and the content relations 26 to provide a set of recommended media content items that are believed likely to be of particular interest to the user. As preferably presented in the browser 14, the user may variously navigate the set of recommendations, including requesting samples of particular content items. A database of content samples 28 may be provided as part of the server computer system 18 directly or, in the contemplated preferred embodiment of the present invention, as a logical component of the server system 18 supported or hosted externally by a source provider, content management, or other party. In either event, the content samples are returned to the user browser 14 for presentation to the user. Based on the review and consideration of the recommendation set, including as applicable any presented content samples, the user may request a further search and production of a new recommendation set, typically identifying a prior recommendation media content item as part of the request, or request the purchase and delivery of a media content item.

In accordance with the present invention, the user navigation of a presented recommendation set and the user actions in reviewing and considering individual and groups of media content items are utilized in the progressive modification and refinement of the profiles data 24. The navigation events received by the server system 18 and the requests for additional information and content samples 28 are readily monitored. Other information can be derived from periods of user non-action, particularly after some media content item information or content samples is requested. That is, the amount of time spent by a user apparently reviewing some biographical information about a particular media item, or the time spent listening to a music clip provides implicit information regarding the interest level of the user in a particular media content item. By extension, this implicit level of interest can also be used to imply a likely level of interest in other media content items with similar characterizing attributes. The implicit information gathered from user actions is preferably processed 22 and stored as an addition and refinement of the profile data 24 previously stored.

Where the review and consideration of some recommended media content item prompts a user purchase decision, a user may execute an electronic purchase transaction (not shown) leading to the delivery 30 of the chosen media content item to the user computer system 12. The delivered media content item is preferably obtained from a third-party content fulfillment server or other similar service. The delivery component 30 may be implemented by a separate distribution service provider or by the content fulfillment service provider.

An overview of the process implemented in a preferred embodiment of the present invention is shown in FIG. 2. The process 36 operates to encourage users 38 to provide source information 40 as at least the initial basis for directing the production of a recommendation set. This information 40 may provide express indications of the interest level in different types and instances of media content and media content items, such as media tracks, artists, and collections. These indications or ratings are stored for both general use in connection with the production of recommendation sets for all users and specifically in regard to productions for the respective users. The ratings are preferably stored as user profiles 24.

Input requests from a user 38, such as requests to find media content in some way similar to an identified media content item, are submitted for processing through system processes 42 to produce a responsive recommendation set back to the use 38. Pre-defined content relationships 26 are retrieved and evaluated in connection with the system processes 42. The actions of the user 38 in browsing recommendation sets are also considered by the system processes 42 as reflecting, at least to some degree, the interests of users regarding particular media content items presented in the recommendations sets. These reflected and thus implied levels of interest are preferably quantified and qualified by the system processes 42 with the resulting information being incorporated into the user and group profiles 24.

In a preferred embodiment of the present invention, the system processes 42 utilize a number of work tables 44 through the process of preparing recommendation sets. These work tables 44 provide temporary and modifiable storage of intermediary relations between potentially recommendable media content items. Thus, in a preferred embodiment of the present invention, the system processes 42 may take multiple approaches to generating a recommendation set and subsequently combine the results of these approaches to produce the recommendation set presented to the user 38. Once such intermediary results approach may consider the content relationships between items the user 38 has rated as highly interesting, or enjoyable, and other items identifiable through the content relations database 26 as having similar characterizing attributes. Another intermediary results approach may concentrate first on correlating user profiles as a basis of media content items rated highly or broadly that are not identifiably known to the user 38.

In the first case, a user 38 selects a media content item known and of interest to the user from a master list of media content items. The selection is submitted to the system processes 42 for autonomous consideration against those items identifiable through the content relations database 26 that are linked by some association, such as particular or cumulatively considered characterizing attributes, to the media content item selected by the user 38. The content relations database 26 provides qualifying information, reflecting the strength or weight of each attribute relationship, as well as identifying the linking relationships. As the product of this autonomous consideration, the system processes 42 produce a set of media content items that, as considered, have the strongest relationship conn