Three-way media recommendation method and system
First Claim
1. An automated recommendation system, comprisinga processor connected to receive resource data defining available resources and at least two sets of profile data, each defining a user'"'"'s preferences with respect to the resources;
- each of the sets of profile data being derived from a different class of interaction of the user with a first portion of the resource data and usable to predict a given resource'"'"'s desirability based on each of the sets;
the processor being adapted to;
generate at least two sets of predictions based on one or a combination of the sets of profile data, andcombine the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions.
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Accused Products
Abstract
An electronic programming guide (EPG) system employing a preference engine and processing system that combines explicit rule profile, history profile, and feedback profile data to generate new predictions. Television shows are presumed to be indexed by many features. These features are extracted and counted for TV shows watched (implicit profile), and for TV shows rated by the viewer (feedback profile). These profiles are straightforward to combine with suitably greater weight being given to the feedback information. In addition, explicit profiles can make recommendations that stand alone or may be used to modify recommendations arising from either of the two sources. The modifications may take the form of additive or multiplicative changes to the existing recommendations or some other suitable mathematical form.
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Citations
26 Claims
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1. An automated recommendation system, comprising
a processor connected to receive resource data defining available resources and at least two sets of profile data, each defining a user'"'"'s preferences with respect to the resources; -
each of the sets of profile data being derived from a different class of interaction of the user with a first portion of the resource data and usable to predict a given resource'"'"'s desirability based on each of the sets; the processor being adapted to; generate at least two sets of predictions based on one or a combination of the sets of profile data, and combine the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method of recommending resources, comprising:
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generating by a processing device, at least two sets of profile data based on expressed preferences of a user with respect to the resources, each being usable to predict a given resource'"'"'s desirability based on each of the sets; generating by a processing device, at least two sets of predictions based on one or a combination of the sets of profile data; and combining, by a processing device, the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17)
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18. An automated recommendation system, comprising:
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a processor connected to receive resource data defining available resources and sets of profile data, each defining a users preferences with respect to the resources; the sets of profile data including; a set of explicit profile data indicating express indications by a user of preferred classes of programming rather than indications by the user of particular resources that are preferred; feedback data set derived from ratings provided by the user with respect to a particular resource in the resource data; and an implicit data set derived from machine-observation of a users resource use history, whereby the implicit data reflects the user'"'"'s selection; the processor being adapted to generate at least two sets of predictions based on one or a combination of the sets of profile data, each of the predictions including a confidence level; the processor being further adapted to combine the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions. - View Dependent Claims (19, 20)
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21. A method of automatically recommending resources, comprising:
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receiving resource data defining available resources and sets of profile data, each defining user preferences with respect to the resources; the sets of profile data including; a set of explicit profile data indicating express indications by a user of preferred classes of programming rather than indications by the user of particular resources that are preferred; a feedback data set derived from ratings provided by the user with respect to a particular resource in the resource data; and an implicit data set derived from machine-observation of a user'"'"'s resource use history, whereby the implicit data reflects the user'"'"'s selection; generating at least two sets of predictions based on one or a combination of the sets of profile data, each of the predictions including a confidence level; and combining the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions to produce a combined set. - View Dependent Claims (22, 23)
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24. A method of combining profile data, comprising:
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generating first profile data by receiving through a user interface user preferences in the form of expressed generalized preferences corresponding classes of resources; generating second profile data by receiving user preferences in the form of rating data corresponding to specific resources; and applying the first and second profile data to respective prediction engines to produce first and second prediction results and combining the first and second results. - View Dependent Claims (25, 26)
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Specification