Koninklijke Philips Corporation N.v.

Pace, Incorporated

Other

Iac Search and Media, Inc.

J Koninklijke Philips Electronics, Nv.

Kaushal Kurapati

Srinivas Gutta

J David Schaffer

Jacquelyn Annette Martino

Karen Irene Trovato

Antonio Colmenarez

James David Schaffer

Miroslav Trajkovic

Nevenka Dimitrova

Alan Levin

Kwok Pun Lee

Apostolos Gerasoulis

Tomasz Imielinski

Lira Nikolovska

Alison F Camplin

Alexandrin Popescul

Srinivas V R Gutta

A television programming recommender combines the explicit viewing preferences, i.e. user specified preferences, of viewers with their television viewing behavior (implicit preferences) to generate program recommendations. The explicit viewing preferences are utilized to generate an explicit recommendation score, E, for an upcoming television program. The implicit viewing preferences are utilized to generate an implicit recommendation score, I, for the program. A combined recommendation score, C, is computed based on the explicit and implicit scores, E and I. The combined recommendation score, C, can be computed using a weighted linear mapping.

Method and apparatus for generating recommendation scores using implicit and explicit viewing preferences

A program recommendation system employing a program record module and one of various nearest neighbor modules is disclosed. In response to a reception of a program record, the program record module converts each key field of the program record into a feature value. A single neighbor module selectively generates a recommendation of a program corresponding to the program record based upon a stored program record qualifying as a nearest neighbor of the received program record. A multiple neighbor module selectively generates a recommendation of the program corresponding to the program record based upon N number of stored program records qualifying as N number of nearest neighbors of the received program record. A neighbor cluster selectively generates a recommendation of the program corresponding to the program record based upon the cluster of stored program records qualifying as the nearest neighbor of the received program record.

Nearest neighbor recommendation method and system

In customizing a user profile employed by a recommendation system, users are prompted for feedback regarding content that is the subject of the recommendation system. Only feedback that does not degrade performance of the recommendation system, as measured by the error rate, is accepted and utilized to modify the user profile. Feedback that would degrade performance is discarded without being employed to alter the user profile. In this manner, error is continually driven toward a minimum by system changes based on feedback.

Method for using only feedback from shows that improve the performance of the recommender system

A system for a meta-browser includes a receiver, an output device for displaying a virtual unified browsing space, and a selector input device for navigating the space and making selections therein. The receiver is provided with the capability to receive information from multiple sources of different media types, including broadcast programming, additional information through the broadcast, electronic program guide information, and the Internet. The meta-browser presents personalized collections of information from multiple sources of different media types as different media collections in the unified browsing space, which can be easily and intuitively browsed. A user's browsing experience is further enhanced by personalized recommendations, which are generated based on a profile of the user's interests captured across multiple media sources. Collaborative filtering, which uses multiple user profiles to compute co-relations between the user's profile and others, is also used to make recommendations. The unified browsing space is also searchable.

Method and apparatus for realizing personalized information from multiple information sources

A television programming recommender is disclosed that selectively obtains feedback information from a user to update one or more profiles associated with the user. Previously obtained implicit and explicit preferences are utilized to selectively focus the collection of feedback information to further update and refine the implicit and explicit preferences. The present invention obtains feedback from a user in a manner that maximizes the value of the obtained information and improves the performance of the television programming recommender. The present invention automatically requests feedback from the user upon the occurrence of predefined criteria. The nature of the requested feedback, and the manner in which the obtained feedback is used to adjust a profile, can vary.

Method and apparatus for selective updating of a user profile

Methods and systems for generating query and result-based relevance indexes are provided. For one embodiment, a plurality of queries is received from a plurality of users. Each query of the plurality of queries is measured based on one or more metrics. The measured data of each query is stored. The queries are associated into topical query categories. A performance of a first query category is calculated based on at least one metric of the one or more metrics. A metric data request for a select category is received from a client. Lastly, the stored measured data of the selected category is transmitted to the client in response to the metric data request.

Methods and systems for generating query and result-based relevance indexes

A method and apparatus are disclosed that generate television program recommendations based on queries that have previously been performed by a user on an electronic program guide. A conventional program recommender score for a given program is adjusted according to the degree of correlation between the attribute-value pairs that define the program and the attribute-value pairs that have previously been searched by the user. A historical search database indicates the number of times each attribute-value pair appears in a user query and provides additional information regarding the preferences of the user. Higher frequency counts for certain attribute-value pairs imply the user's preference for programs conforming to such criteria.

Method and apparatus for generating television program recommendations based on prior queries

A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. A distance computation routine evaluates the closeness of a television program to each cluster based on the distance between a given television program and the mean of a given cluster.

Method and apparatus for evaluating the closeness of items in a recommender of such items

A television program recommender is disclosed that automatically identifies changes in viewing preferences. Once changing viewing preferences are identified, the disclosed television programming recommender can (i) adapt the generated television program recommendations to such changes in viewing preferences, or (ii) more efficiently manage the storage of the viewing history. For cyclical or periodic changes in viewing preferences, the television programming recommender generates television program recommendations using a sub-set of the viewing history from a corresponding earlier time period. Likewise, for true or permanent changes in viewing preferences, the television programming recommender optionally generates television program recommendations using the most recent sub-set of the viewing history, which most likely reflects the current viewing preferences. In a further variation, program recommendations can be generated using a combination, such as the union or intersection, of recommendations based on viewing histories from two different periods of time. The television programming recommender can confirm that viewing preferences have not changed significantly over time, and thereafter delete portions of the viewing history without loss of any performance in the generated recommendations.

Television program recommender with automatic identification of changing viewer preferences

A dynamic method and system for processing video source frames (e.g., a television movie) with a video processing system (VPS) that includes a processor, a memory structure, input devices, and an output display. The video source frames, which are received by the VPS from a video source, are executed by the processor. While the video source frames are being executed, a subset of the frames, called key frames, are extracted from the video source frames and stored in the memory structure. The extracting of the key frames is implemented in accordance with a frame extraction algorithm. The extracting is terminated prior to completion of execution of the video source frames. Following termination of extracting, the key frames are reviewed by a user with the output display to determine whether to watch the remainder of the video source frames. The key frames are erased after being reviewed.

Dynamic key frame generation and usage

An apparatus and method for recommending a schedule of events to a user is disclosed. In the preferred embodiment of the system and method, each channel schedule is broken down into time slices. A novel fuzzy-now recommendation-time value is calculated for each time slice. This fuzzy-now recommendation-time value is a two dimensional value measured in units of recommendation-time, or 'enjoyment minutes'. By means of the calculated fuzzy-now recommendation-time values, recommended schedules may be generated using a wide variety of selection methods.

Evening planner

A recommendation system and method are disclosed. In the system and method, the personal schedule of the user is used to modify the recommendation functions of media events. The personal schedule may be entered by the user or determined through monitoring over time. An exemplary recommendation function modification is if a media event ends after the user's bedtime, as indicated by the personal schedule. In this example, the recommendation function of that event will be reduced in value because the user will likely go to bed before the event is over.

Real-time event recommender for media programming using 'Fuzzy-Now' and 'Personal Scheduler'

A method, system and program product for populating a user profile based on existing user profiles is provided. Specifically, under the present invention, base characteristics are designated for a new user profile. Based on the designated characteristics, the new user profile is associated with a particular Vornoi cluster region of existing user profiles. Once associated, the new user profile is populated with defined characteristics from the existing user profiles within the particular cluster region. After population, viewing recommendations can be made.

Method, system and program product for populating a user profile based on existing user profiles

Real-time events are electronically recommended using a fuzzy-now function of time. Real-time experiencing apparatus can be automatically tuned to a particular real-time event using a surf ring related to recommendations or automatically without a user request. Recommendations can be presented on a remote control device.

Recommender system using 'fuzzy-now' for real-time events

A method and apparatus are disclosed for generating television program recommendations based on a user-specified query. The television programming recommender automatically constructs a query based on previous searches that have been executed by a user. The television programming recommender can automatically initiate a search in response to a user command using the top-N search terms for each attribute that have been previously used in a query. Each time a search is initiated by the user the query is decomposed to identify the attribute-value pairs specified by the user and a counter is incremented. When an automatic search is initiated, the top-N attribute-value pairs are retrieved for each possible attribute and automatically placed in a search bin. The attributes of each program in an electronic program guide are compared to the attribute-value pairs specified in the automatic query to identify programs satisfying the limitations of the automatic query.

Growing community of inventors

Yorktown Heights, NY, United States of America

Kaushal Kurapati