Collaborative filtering-based recommendations
First Claim
Patent Images
1. A computer-implemented method comprising:
- choosing item recommendations for a plurality of users comprising at least a first user and a second user; and
outputting the item recommendations;
wherein choosing item recommendations for a plurality of users comprises;
for the first user, identifying, out of a plurality of possible user similarity measures, a first user similarity measure as most accurately identifying users who provided rating information similar to rating information provided by the first user;
for the first user, using the first user similarity measure to identify, from the plurality of users, a first set of similar users who provided rating information similar to the rating information provided by the first user;
for the first user, using the rating information provided by the first set of similar users to select a first item recommendation;
for the second user, identifying, out of the plurality of possible user similarity measures, a second user similarity measure as most accurately identifying users who provided rating information similar to rating information provided by the second user, wherein the second user similarity measure is different than the first user similarity measure;
for the second user, using the second user similarity measure to identify, from the plurality of users, a second set of similar users who provided rating information similar to the rating information provided by the second user; and
for the second user, using the rating information provided by the second set of similar users to select a second item recommendation.
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Abstract
Various techniques can be used to implement a collaborative filtering-based recommendation engine. For example, different similarity measures can be used for different users. Different similarity measures can be used for a particular user across time. A superior similarity measure can be found for a user. User-defined similarity measures can be supported.
51 Citations
15 Claims
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1. A computer-implemented method comprising:
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choosing item recommendations for a plurality of users comprising at least a first user and a second user; and outputting the item recommendations; wherein choosing item recommendations for a plurality of users comprises; for the first user, identifying, out of a plurality of possible user similarity measures, a first user similarity measure as most accurately identifying users who provided rating information similar to rating information provided by the first user; for the first user, using the first user similarity measure to identify, from the plurality of users, a first set of similar users who provided rating information similar to the rating information provided by the first user; for the first user, using the rating information provided by the first set of similar users to select a first item recommendation; for the second user, identifying, out of the plurality of possible user similarity measures, a second user similarity measure as most accurately identifying users who provided rating information similar to rating information provided by the second user, wherein the second user similarity measure is different than the first user similarity measure; for the second user, using the second user similarity measure to identify, from the plurality of users, a second set of similar users who provided rating information similar to the rating information provided by the second user; and for the second user, using the rating information provided by the second set of similar users to select a second item recommendation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13)
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10. One or more computer-readable non-transitory storage media having encoded thereon computer-executable instructions for performing a computer-implemented method comprising:
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choosing item recommendations for different users via different user similarity metrics when applying collaborative filtering for the different users, wherein a user similarity metric is designated for one user independently of other users; and outputting the item recommendations; wherein choosing item recommendations for different users comprises (a)-(e); (a) for a first user, identifying, out of a plurality of possible user similarity metrics, a first user similarity metric measuring similarity among users, wherein the identifying is based at least on performance of the first user similarity metric with respect to the first user; (b) for the first user, using the first user similarity metric to identify a most similar user; (c) choosing a first recommendation for the first user via collaborative filtering using the first user similarity metric measuring similarity among users, wherein a weight assigned to an influence of the most similar user is greater than a weight assigned to an influence of any other user when choosing a first recommendation for the first user via collaborative filtering; (d) for a second user, identifying, out of the possible user similarity metrics, a second user similarity metric measuring similarity among users, wherein the identifying is based at least on performance of the second user similarity metric with respect to the second user, wherein the second user similarity metric is different from the first; and (e) choosing a second recommendation for the second user via the second user similarity metric measuring similarly among users; wherein the first user similarity metric is implemented as a first similarity function that provides a value indicating distance between users; and the second user similarity metric is implemented as a second similarity function different from the first similarity function.
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14. A computer-implemented method comprising:
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choosing item recommendations for different users via different user similarity metrics when applying collaborative filtering for the different users, wherein a user similarity metric is designated for one user independently of other users; and outputting the item recommendations; wherein choosing item recommendations for different users comprises (a)-(e); (a) for a first user, identifying, out of a plurality of possible user similarity metrics, a first user similarity metric measuring similarity among users, wherein the identifying is based at least on performance of the first user similarity metric with respect to the first user; (b) for the first user, using the first user similarity metric to identify a most similar user; (c) choosing a first recommendation for the first user via collaborative filtering using the first user similarity metric measuring similarity among users, wherein a weight assigned to an influence of the most similar user is greater than a weight assigned to an influence of any other user when choosing a first recommendation for the first user via collaborative filtering; (d) for a second user, identifying, out of the possible user similarity metrics, a second user similarity metric measuring similarity among users, wherein the identifying is based at least on performance of the second user similarity metric with respect to the second user, wherein the second user similarity metric is different from the first; and (e) choosing a second recommendation for the second user via the second user similarity metric measuring similarly among users; wherein the first user similarity metric is implemented as a first similarity function that provides a value indicating distance between users; and the second user similarity metric is implemented as a second similarity function different from the first similarity function.
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15. One or more computer-readable non-transitory storage media having encoded thereon computer-executable instructions for performing a computer-implemented method comprising:
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choosing item recommendations for a plurality of users comprising at least a first user and a second user; and outputting the item recommendations; wherein choosing item recommendations for a plurality of users comprises; for the first user, identifying, out of a plurality of possible user similarity measures, a first user similarity measure as most accurately identifying users who provided rating information similar to rating information provided by the first user; for the first user, using the first user similarity measure to identify, from the plurality of users, a first set of similar users who provided rating information similar to the rating information provided by the first user; for the first user, using the rating information provided by the first set of similar users to select a first item recommendation; for the second user, identifying, out of the plurality of possible user similarity measures, a second user similarity measure as most accurately identifying users who provided rating information similar to rating information provided by the second user, wherein the second user similarity measure is different than the first user similarity measure; for the second user, using the second user similarity measure to identify, from the plurality of users, a second set of similar users who provided rating information similar to the rating information provided by the second user; and for the second user, using the rating information provided by the second set of similar users to select a second item recommendation.
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Specification