Time-centric training, inference and user interface for personalized media program guides
First Claim
1. A system for ranking items in a selectable information list received from an information delivery system, comprising:
- a database system that logs selections of information viewed by local users of the information delivery system and tags each of the logged selections of information with a corresponding time subinterval from a plurality of time subintervals that relates to a respective viewing of the selected information;
a filtering component that forms a temporally filtered reviewed items list that includes a subset of the logged selections of information viewed by the local users, the subset chosen to incorporate the logged selections tagged with a particular one of the plurality of time subintervals that includes a target time period for providing a recommendation, the temporally filtered reviewed items list provides implicit evidence of content preferences associated with a likely subset of the local users that employs the information delivery system during the particular one of the plurality of time subintervals;
a collaborative filtering system that infers the content preferences associated with the likely subset of the local users by utilizing the subset of the logged selections included in the temporally filtered reviewed items list as an input, and generates the recommendation specific to the inferred, likely subset of the local users based at least in part on the inferred content preferences and information obtained from a plurality of global users related to the particular one of the plurality of time subintervals, wherein the filtering component comprises a popularity filter that selects a recommendation based, at least in part, by multiplying a collaborative filter score of a recommendation by the probability that the user does not know of the recommendation; and
a user interface that displays the recommendation.
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Abstract
The present invention is related to a system and method of considering time segments or intervals in a collaborative filtering model. The present invention extends collaborative filtering approaches by integrating considerations of temporality into the training and/or vote input associated with the usage of collaborative filtering models. The present invention also applies filtering to the output with temporal models, so as to view a most appropriate subset of recommended content, centering on content that may be available at a target time. The present invention applies time to a collaborative filtering model by allowing weight to be associated with selections within a current time segment, selections historically watched within the current time segment by the user and selections historically watched within the current time segment by a large group of users.
143 Citations
17 Claims
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1. A system for ranking items in a selectable information list received from an information delivery system, comprising:
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a database system that logs selections of information viewed by local users of the information delivery system and tags each of the logged selections of information with a corresponding time subinterval from a plurality of time subintervals that relates to a respective viewing of the selected information; a filtering component that forms a temporally filtered reviewed items list that includes a subset of the logged selections of information viewed by the local users, the subset chosen to incorporate the logged selections tagged with a particular one of the plurality of time subintervals that includes a target time period for providing a recommendation, the temporally filtered reviewed items list provides implicit evidence of content preferences associated with a likely subset of the local users that employs the information delivery system during the particular one of the plurality of time subintervals; a collaborative filtering system that infers the content preferences associated with the likely subset of the local users by utilizing the subset of the logged selections included in the temporally filtered reviewed items list as an input, and generates the recommendation specific to the inferred, likely subset of the local users based at least in part on the inferred content preferences and information obtained from a plurality of global users related to the particular one of the plurality of time subintervals, wherein the filtering component comprises a popularity filter that selects a recommendation based, at least in part, by multiplying a collaborative filter score of a recommendation by the probability that the user does not know of the recommendation; and a user interface that displays the recommendation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A system for ranking items in a selectable information list received from an information delivery system, comprising:
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means for logging selections of information viewed by local users of the information delivery system and temporal history related to time segments within a day that correspond to the viewing of the selected information, the selections of information logged for a plurality of days; means for training a plurality of separate collaborative filtering models, each with information from a corresponding, respective time segment within a day that has been viewed by the local users and disparate logged temporal history that has been viewed by a plurality of global users; means for inferring content preferences associated with a likely subset of the local users that employs the information delivery system during a particular time segment within a day utilizing a respective one of the collaborative filtering models corresponding to a target time period to provide a recommendation; means for generating the recommendation specific to the inferred, likely subset of the local users based at least in part on the inferred content preferences and information obtained from a plurality of global users related to the particular time segment within a day, wherein generating the recommendation further comprises a popularity filter that selects a recommendation based, at least in part, by multiplying a collaborative filter score of a recommendation by the probability that the user does not know of the recommendation; means for automatically broadening to include at least one additional time segment within a day when the recommendation yielded from the particular time segment within a day covering the target time period is inadequate; and means for displaying the recommendation.
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Specification