Processes for improving the utility of personalized recommendations generated by a recommendation engine
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 item preference data, said recommendation set comprising a computer representation of items predicted to be of interest to the target user;
generating a filtered recommendation set by using item similarity data to filter the recommendation set, said filtered recommendation set having a greater degree of item diversity than the recommendation set, wherein generating the filtered recommendation set comprises identifying a fuzzy duplicate item pair in the recommendation set, and removing one item of said fuzzy duplicate item pair from the recommendation set; and
outputting information regarding at least some of the items in the filtered recommendation set for presentation to the target user.
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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.
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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 item preference data, said recommendation set comprising a computer representation of items predicted to be of interest to the target user; generating a filtered recommendation set by using item similarity data to filter the recommendation set, said filtered recommendation set having a greater degree of item diversity than the recommendation set, wherein generating the filtered recommendation set comprises identifying a fuzzy duplicate item pair in the recommendation set, and removing one item of said fuzzy duplicate item pair from the recommendation set; and outputting information regarding at least some of the items in the filtered recommendation set for presentation to the target user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer system comprising:
one or more computers programmed with executable code modules to at least; retrieve item preference data for a target user from computer storage, said item preference data reflective of item preferences of the target user; generate a recommendation set using the item preference data, said recommendation set comprising a computer representation of items predicted to be of interest to the target user; generate a filtered recommendation set by using item similarity data to filter the recommendation set, said filtered recommendation set having a greater degree of item diversity than the recommendation set, wherein generating the filtered recommendation set comprises identifying a fuzzy duplicate item pair in the recommendation set, and removing one item of said fuzzy duplicate item pair from the recommendation set; and output information regarding at least some of the items in the filtered recommendation set for presentation to the target user.
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11. A computer-implemented method, comprising:
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receiving 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; and filtering the recommendation set to prevent at least some items that have a threshold degree of similarity to each other from being recommended to the target user in combination, said threshold set so as to prevent, for at least some fuzzy duplicate items pairs, both items of the pair from being recommended in combination, said filtering performed by execution of program code by one or more processors. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A computer-readable medium which stores a computer program comprising executable instructions that direct a computer 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; and filter the recommendation set to prevent at least some items that have a threshold degree of similarity to each other from being recommended to the target user in combination, said threshold set so as to prevent, for at least some fuzzy duplicate items pairs, both items of the pair from being recommended in combination.
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18. A recommendation system, comprising:
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a recommendation engine configured to generate personalized item recommendations for target users based on item preference data stored for said target users; and a similar items filter configured to filter the personalized item recommendations generated by the recommendation engine based on item similarities data to prevent at least some items that are fuzzy duplicates of each other from being recommended in combination to a target user, said similar items filter thereby increasing a diversity of personalized recommendations presented to target users; said recommendation engine and similar items filter comprising computer hardware that executes software. - View Dependent Claims (19, 20, 21, 22)
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Specification