Increasing the diversity of item recommendations by filtering
First Claim
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1. A computer-implemented method, comprising:
- retrieving item preference data for a target user from computer storage, said item preference data reflective of item preferences of the target user;
generating a recommendation set using the retrieved item preference data, said recommendation set comprising a computer representation of items predicted to be of interest to the target user;
filtering the recommendation set, wherein filtering the recommendation set comprises filtering out a first item from the recommendation set based at least partly on a determination that the first item has at least a threshold degree of similarity to a second item in the recommendation set, said first and second items not being duplicates of each other, said filtering producing a filtered recommendation set that has a higher degree of item diversity than the recommendation set; and
outputting at least a portion of the filtered recommendation set for presentation to the target user with a display element that enables the target user to initiate a display of one or more items, including said first item, that were filtered from the recommendation set;
said method performed by a computer system that comprises one or more computers.
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Abstract
A recommendation system increases the diversity of item recommendations provided to a target user by using item similarity data to filter an initial recommendation set. In one embodiment, selected items are filtered from the initial recommendation set based on similarity scores that represent degrees of similarity between particular items. The similarity scores may be generated based on an automated comparison of item attributes or content, or based on another measure of item similarity.
23 Citations
22 Claims
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1. A computer-implemented method, comprising:
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retrieving item preference data for a target user from computer storage, said item preference data reflective of item preferences of the target user; generating a recommendation set using the retrieved item preference data, said recommendation set comprising a computer representation of items predicted to be of interest to the target user; filtering the recommendation set, wherein filtering the recommendation set comprises filtering out a first item from the recommendation set based at least partly on a determination that the first item has at least a threshold degree of similarity to a second item in the recommendation set, said first and second items not being duplicates of each other, said filtering producing a filtered recommendation set that has a higher degree of item diversity than the recommendation set; and outputting at least a portion of the filtered recommendation set for presentation to the target user with a display element that enables the target user to initiate a display of one or more items, including said first item, that were filtered from the recommendation set; said method performed by a computer system that comprises one or more computers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for generating recommendations, the system comprising:
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a data repository of item data, said item data including item attributes of a plurality of items; an item comparison system configured to use the item data, including the item attributes, to generate similarity scores for particular item pairs, each similarity score representing a degree to which two particular items are similar to each other; a data repository of item similarity data, said item similarity data identifying item pairs associated with similarity scores satisfying a selected threshold; a recommendation engine configured to generate a recommendation set based on user preference data associated with a target user, said recommendation set comprising a computer representation of items predicted to be of interest to the target user; a similar items filter configured to use the data repository of item similarity data to identify, in said recommendation set, one or more item pairs whose respective similarity scores satisfy said threshold, said similar items filter additionally configured to filter out, from the recommendation set, one item of each such identified item pair, to thereby prevent certain items from being recommended to the target user in combination, said similar items filter comprising a processor; and a server configured to generate, for display to the target user, a page that lists at least a portion of the filtered recommendation set, said page including one or more display elements for enabling the target user to initiate the display of one or more items filtered out from the recommendation set. - View Dependent Claims (12, 13, 14, 15)
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16. Physical computer storage that stores executable code that directs a computing system to at least:
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receive a recommendation set generated by a recommendation engine, said recommendation set comprising a computer representation of a plurality of items predicted to be of interest to a target user; filter the recommendation set to increase a degree of item diversity of the recommendation set, said filtering comprising filtering out a first item from the recommendation set based at least partly on a determination that the first item has at least a threshold degree of similarity to a second item in the recommendation set, said first and second items not being duplicates of each other; and output at least a portion of the filtered recommendation set for presentation to the target user with a display element that enables the target user to initiate a display of one or more items, including said first item, that were filtered from the recommendation set. - View Dependent Claims (17, 18, 19, 20, 21, 22)
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Specification