Item recommendation service
First Claim
1. A computer-implemented method of selecting items to recommend to a user, the method comprising:
- identifying a plurality of items that, based on activity of the user, are deemed to be of interest to the user;
for each of said items of interest, retrieving, from a pre-generated data structure that maps items to related items, a respective related items list;
weighting the related items lists based on information reflective of the user'"'"'s affinity for the corresponding items of interest, such that at least some of the related items lists are weighted differently than others, wherein weighting a related items list comprises weighting individual items on the related items lists, and wherein the amounts by which the items are weighted influences a likelihood that such items will be selected to recommend to the user, wherein weighting the related items lists comprises at least one of the following;
(1) weighting a related items list by an amount that depends upon an amount of time that has transpired since the user performed an action that evidences the user'"'"'s affinity for the corresponding item of interest, and (2) weighting a related items list by an amount that depends upon an explicit rating by the user of the corresponding item of interest;
generating a recommendations list at least partly by combining the weighted related items lists, said recommendations lists including data values for respective items thereon, each data value being dependent upon (1) whether the respective item is on more than one of the related items lists, and (2) the weighting applied to the related items list or lists on which the respective item appears; and
selecting, based at least partly on said data values, a subset of the items on the recommendations list to recommend to the user;
said method performed by a computing system that comprises one or more physical servers.
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Accused Products
Abstract
A computer-implemented recommendation service uses item-to-item relationship mappings to select items to recommend to the user. The item-to-item relationship mappings may reflect user-behavior-based (e.g., purchase-based) item relationships, content-based item relationships, or a combination thereof. In one embodiment, personalized recommendations are generated for a user by a process that comprises retrieving from the mapping, for each of a plurality of items of interest to the user, a respective related items list; weighting the related items lists based on information regarding the user'"'"'s affinity for the corresponding items of interest; combining the weighted related items lists to form a pool of scored items, and selecting items from the pool to recommend to the user.
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Citations
24 Claims
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1. A computer-implemented method of selecting items to recommend to a user, the method comprising:
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identifying a plurality of items that, based on activity of the user, are deemed to be of interest to the user; for each of said items of interest, retrieving, from a pre-generated data structure that maps items to related items, a respective related items list; weighting the related items lists based on information reflective of the user'"'"'s affinity for the corresponding items of interest, such that at least some of the related items lists are weighted differently than others, wherein weighting a related items list comprises weighting individual items on the related items lists, and wherein the amounts by which the items are weighted influences a likelihood that such items will be selected to recommend to the user, wherein weighting the related items lists comprises at least one of the following;
(1) weighting a related items list by an amount that depends upon an amount of time that has transpired since the user performed an action that evidences the user'"'"'s affinity for the corresponding item of interest, and (2) weighting a related items list by an amount that depends upon an explicit rating by the user of the corresponding item of interest;generating a recommendations list at least partly by combining the weighted related items lists, said recommendations lists including data values for respective items thereon, each data value being dependent upon (1) whether the respective item is on more than one of the related items lists, and (2) the weighting applied to the related items list or lists on which the respective item appears; and selecting, based at least partly on said data values, a subset of the items on the recommendations list to recommend to the user; said method performed by a computing system that comprises one or more physical servers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An item recommendation system, comprising:
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physical, non-transitory computer storage that stores a mapping that maps individual items to respective sets of related items; and a recommendation component that is operative to generate personalized recommendations for a user by a process that comprises; identifying a plurality of items of interest to the user; for each item of interest, obtaining, from the mapping, a respective related items list; weighting the related items lists based on information reflective of the user'"'"'s affinity for the corresponding items of interest, wherein weighting a related items list comprises weighting individual items on the related items lists, and wherein the amounts by which the items are weighted influences a likelihood that such items will be selected to recommend to the user, wherein weighting the related items lists comprises at least one of the following;
(1) weighting a related items list by an amount that depends upon an amount of time that has transpired since the user performed an action that evidences the user'"'"'s affinity for the corresponding item of interest, and (2) weighting a related items list by an amount that depends upon an explicit rating by the user of the corresponding item of interest;generating a recommendations pool at least partly by combining the weighted related items lists, said recommendations pool including data values for respective items in the pool, each data value being dependent upon (1) whether the respective item is on more than one of the related items lists, and (2) the weighting applied to the related items list or lists on which the respective item appears; and selecting, based at least partly on said data values, a subset of the items on the recommendations list to recommend to the user. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. Physical, non-transitory computer storage having stored thereon executable code that directs a computer system to perform a process that comprises:
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identifying a plurality of items that, based on activity of a user, are deemed to be of interest to the user; for each of said items of interest, retrieving, from a pre-generated data structure that maps items to related items, a respective related items list; weighting the related items lists based on information reflective of the user'"'"'s affinity for the corresponding items of interest, such that at least some of the related items lists are weighted differently than others, wherein weighting a related items list comprises weighting individual items on the related items lists, and wherein the amounts by which the items are weighted influences a likelihood that such items will be selected to recommend to the user, wherein weighting the related items lists comprises at least one of;
(1) weighting a related items list by an amount that depends upon an amount of time that has transpired since the user performed an action that evidences the user'"'"'s affinity for the corresponding item of interest, and (2) weighting a related items list by an amount that depends upon an explicit rating by the user of the corresponding item of interest;generating a recommendations list at least partly by combining the weighted related items lists, said recommendations lists including data values for respective items thereon, each data value being dependent upon (1) whether the respective item is on more than one of the related items lists, and (2) the weighting applied to the related items list or lists on which the respective item appears; and selecting, based at least partly on said data values, a subset of the items on the recommendations list to recommend to the user. - View Dependent Claims (22, 23, 24)
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Specification