Real-time online-learning object recommendation engine
First Claim
1. A method comprising:
- by one or more computing devices, receiving a request from a first user for a content page;
by one or more computing devices, determining a page identifier for the requested content page;
by one or more computing devices, determining, based on the page identifier, one or more second users, wherein the one or more second users are identified in the requested content page;
by one or more computing devices, determining a plurality of content items based on one or more of the second users, wherein each of the plurality content items is owned by a second user;
by one or more computing devices, determining one or more user features of the first user;
by one or more computing devices, determining for each of the content items one or more content features;
by one or more computing devices, calculating for each of the content items an expected value of the content item with respect to the first user based on the user features of the first user and the content features of the content item;
by one or more computing devices, ordering the content items by decreasing expected value; and
by one or more computing devices, delivering to the first user, with the requested content page, one or more of the content items as recommendations to the first user based on the ordering of the content items.
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Abstract
In one embodiment, a system includes one or more computing systems that implement a social networking environment containing a large number of heterogeneous objects type, each of the plurality of object types having varying features, the system implementing a generic object recommendation engine for scoring objects and recommending the objects to users of the social networking system. In particular embodiments, the user and content object features are fed as inputs into a heuristic model that generates an expected value for the content object and user. In particular embodiments, the object recommendation engine includes an online learner that may log a user'"'"'s actions after the initial impression to determine the relatively degree of interest to the user.
25 Citations
19 Claims
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1. A method comprising:
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by one or more computing devices, receiving a request from a first user for a content page; by one or more computing devices, determining a page identifier for the requested content page; by one or more computing devices, determining, based on the page identifier, one or more second users, wherein the one or more second users are identified in the requested content page; by one or more computing devices, determining a plurality of content items based on one or more of the second users, wherein each of the plurality content items is owned by a second user; by one or more computing devices, determining one or more user features of the first user; by one or more computing devices, determining for each of the content items one or more content features; by one or more computing devices, calculating for each of the content items an expected value of the content item with respect to the first user based on the user features of the first user and the content features of the content item; by one or more computing devices, ordering the content items by decreasing expected value; and by one or more computing devices, delivering to the first user, with the requested content page, one or more of the content items as recommendations to the first user based on the ordering of the content items. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A non-transitory, computer-readable media comprising instructions operable, when executed by one or more computing systems, to:
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receive a request from a first user for a content page; determine a page identifier for the requested content page; determine, based on the page identifier, one or more second users, wherein the one or more second users are identified in the requested content page; determine a plurality of content items based on one or more of the second users, wherein each of the plurality content items is owned by a second user; determine user features of the first user; determine for each of the content items one or more content features; calculate for each of the content items an expected value of the content item with respect to the first user based on the user features of the first user and the content features of the item; order the content items by decreasing expected value; and deliver to the first user, with the requested content page, one or more of the content items as recommendations to the first user based on the ordering of the content items. - View Dependent Claims (17, 18, 19)
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Specification