Mining of user event data to identify users with common interests
First Claim
1. A computer-implemented method of matching users to other users, the method comprising:
- for each of a plurality of users of an electronic catalog, storing, in computer storage, event data representing user-generated events that reflect user affinities for particular items reflected in the electronic catalog;
programmatically generating a score that reflects a degree to which item affinities of a first user of said plurality of users are similar to item affinities of a second user of said plurality of users, said score taking into consideration a first plurality of items corresponding to the item affinities of the first user and a second plurality of items corresponding to the item affinities of the second user, wherein generating the score comprises weighting a first item and a second item identified in both the first and second plurality of items, wherein the first and second items are different, wherein the first and second items are weighted differently based at least in part on a first inherent characteristic of the first item and a second inherent characteristic of the second item, and wherein the first and second inherent characteristics are different; and
based at least in part on the score, programmatically determining whether to recommend the second user to the first user.
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Abstract
A computer-implemented matching service matches users to other users, and/or to user communities, based at least in part on a computer analysis of event data reflective of user behaviors. The event data may, for example, evidence user affinities for particular items represented in an electronic catalog, such as book titles, music titles, movie titles, and/or other types of items that tend to reflect the traits of users. Event data reflective of other types of user actions, such as item-detail-page viewing events, browse node visits, search query submissions, and/or web browsing patterns may additionally or alternatively be considered. By taking such event data into consideration, the matching service reduces the burden on users to explicitly supply personal profile information, and reduces poor results caused by exaggerations and other inaccuracies in such profile information.
308 Citations
23 Claims
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1. A computer-implemented method of matching users to other users, the method comprising:
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for each of a plurality of users of an electronic catalog, storing, in computer storage, event data representing user-generated events that reflect user affinities for particular items reflected in the electronic catalog; programmatically generating a score that reflects a degree to which item affinities of a first user of said plurality of users are similar to item affinities of a second user of said plurality of users, said score taking into consideration a first plurality of items corresponding to the item affinities of the first user and a second plurality of items corresponding to the item affinities of the second user, wherein generating the score comprises weighting a first item and a second item identified in both the first and second plurality of items, wherein the first and second items are different, wherein the first and second items are weighted differently based at least in part on a first inherent characteristic of the first item and a second inherent characteristic of the second item, and wherein the first and second inherent characteristics are different; and based at least in part on the score, programmatically determining whether to recommend the second user to the first user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer-implemented method of matching users to other users, the method comprising:
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for each of a plurality of users, collecting and storing, in computer storage, event data representing user-generated events reflective of user behaviors, said event data collected via a computer network without requiring the users to explicitly provide personal preference information; programmatically generating a score that reflects a degree to which behaviors of a first user of said plurality of users are similar to behaviors of a second user of said plurality of users, said score taking into consideration a first plurality of items corresponding to the behaviors of the first user and a second plurality of items corresponding to the behaviors of the second user, wherein generating the score comprises identifying items ordered in common between the first and second plurality of items and weighting each of the identified items as a function of an inherent characteristic of each item, wherein the identified items comprise a first item having a first inherent characteristic and a second item having a second inherent characteristic, and wherein the first and second items are weighted differently based at least in part on the first inherent characteristic of the first item and the second inherent characteristic of the second item; and based at least in part on the score, programmatically determining whether to recommend the second user to the first user. - View Dependent Claims (16, 17, 18, 19)
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20. A computer-implemented method of matching users to other users, the method comprising:
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for each of a plurality of users, storing, in computer storage, event data representing item selection histories; programmatically generating a score that reflects a degree to which item selections of a first user of said plurality of users are similar to item selections of a second user of said plurality of users, said score taking into consideration a first plurality of items corresponding to items selected by the first user and a second plurality of items corresponding to items selected by the second user, wherein generating the score comprises weighting a first item and a second item identified in both the first and second plurality of items, wherein the first and second items are different, wherein the first and second items are weighted differently based at least in part on a first inherent characteristic of the first item and a second inherent characteristic of the second item, and wherein the first and second inherent characteristics are different; and based at least in part on the score, programmatically determining whether to recommend the second user to the first user. - View Dependent Claims (21, 22, 23)
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Specification