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METHODS AND SYSTEMS FOR SEGMENTING RELATIVE USER PREFERENCES INTO FINE-GRAIN AND COARSE-GRAIN COLLECTIONS

  • US 20070276859A1
  • Filed: 03/06/2007
  • Published: 11/29/2007
  • Est. Priority Date: 03/06/2006
  • Status: Active Grant
First Claim
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1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learned user preferences, the method comprising:

  • providing a set of content items, each content item having at least one associated descriptive term to describe the content item;

    receiving incremental input entered by the user for incrementally identifying desired content items;

    in response to the incremental input entered by the user, presenting a subset of content items;

    receiving selection actions of content items of the subset from the user;

    analyzing the descriptive terms associated with the selected content items to learn the preferred descriptive terms of the user;

    expressing the learned preferred descriptive terms as a segmented probability distribution function having at least one fine grain segment and at least one coarse grain segment, wherein the fine grain segment has fine grain differentiation of probability weights associated with preferred descriptive terms within the segment, and wherein the coarse grain segment has relatively coarse grain differentiation of probability weights associated with preferred descriptive terms within the segment; and

    in response to receiving subsequent incremental input entered by the user, selecting and ordering a collection of content items by promoting the ranking of content items associated with the learned preferred descriptive terms of the user according to the differentiation provided by the segmented probability distribution function.

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