System, method and computer program product for predicting item preference using revenue-weighted collaborative filter
First Claim
1. A method for identifying consumer items more likely to be bought by a user, comprising:
- in a computer having a processor and a memory, wherein the computer is communicatively connected to a computing device;
generating an interface which is presented to the user via the computing device;
determining, responsive to the user viewing a baseline item via the interface on the computing device, for each individual item of a plurality of items, a probability that the user will select the individual item, given that the user has expressed interest in the baseline item, wherein the probability is determined based on;
a similarity among the plurality of items based on observable features thereof;
an aggregate online search behavior relative to individual items across multiple users; and
preferences of the user with respect to the baseline item, wherein the baseline item is viewed by the user; and
ranking, in decreasing order, probabilities determined for the plurality of items, wherein items that are more likely to be bought by the user are ranked higher than items that are less likely to be bought by the user;
determining a probability that the user will select a particular one of the individual items of the plurality of items after the particular baseline item is viewed by the user; and
updating the interface running on the computing device, wherein the updated interface presents to the user at least a portion of the plurality of items ranked in decreasing order according to the probabilities.
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Accused Products
Abstract
Embodiments disclosed provide a system, method, and computer program product for identifying consumer items more likely to be bought by an individual user. In some embodiments, a collaborative filter may be used to rank items based on the degree to which they match user preferences. The collaborative filter may be hierarchical and may take various factors into consideration. Example factors may include the similarity among items based on observable features, a summary of aggregate online search behavior across multiple users, the item features determined to be most important to the individual user, and a baseline item against which a conditional probability of another item being selected is measured.
121 Citations
20 Claims
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1. A method for identifying consumer items more likely to be bought by a user, comprising:
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in a computer having a processor and a memory, wherein the computer is communicatively connected to a computing device; generating an interface which is presented to the user via the computing device; determining, responsive to the user viewing a baseline item via the interface on the computing device, for each individual item of a plurality of items, a probability that the user will select the individual item, given that the user has expressed interest in the baseline item, wherein the probability is determined based on; a similarity among the plurality of items based on observable features thereof; an aggregate online search behavior relative to individual items across multiple users; and preferences of the user with respect to the baseline item, wherein the baseline item is viewed by the user; and ranking, in decreasing order, probabilities determined for the plurality of items, wherein items that are more likely to be bought by the user are ranked higher than items that are less likely to be bought by the user; determining a probability that the user will select a particular one of the individual items of the plurality of items after the particular baseline item is viewed by the user; and updating the interface running on the computing device, wherein the updated interface presents to the user at least a portion of the plurality of items ranked in decreasing order according to the probabilities. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by at least one processor in a computer having a memory, wherein the computer is communicatively connected to a computing device, wherein the instructions are translatable by the at least one processor to perform:
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generating an interface which is presented to a user via the computing device; determining, responsive to the user viewing a baseline item via the interface on the computing device, for each individual item of a plurality of items, a probability that the user will select the individual item, given that the user has expressed interest in the baseline item, wherein the probability is determined based on; a similarity among the plurality of items based on observable features thereof; an aggregate online search behavior relative to individual items across multiple users; and preferences of the user with respect to the baseline item, wherein the baseline item is viewed by the user; ranking, in decreasing order, probabilities determined for the plurality of items, wherein items that are more likely to be bought by the user are ranked higher than items that are less likely to be bought by the user; determining a probability that the user will select a particular one of the individual items of the plurality of items after the particular baseline item is viewed by the user; and updating the interface running on the computing device, wherein the updated interface presents to the user at least a portion of the plurality of items ranked in decreasing order according to the probabilities. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method for identifying consumer items more likely to be bought by a user, comprising:
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in a computer having a processor and a memory, wherein the computer is communicatively connected to a computing device; generating an interface which is presented to the user via the computing device;
determining, responsive to the user viewing a baseline item via the interface on the computing device, for each individual item of a plurality of items, a probability that the user will select the individual item, given that the user has expressed interest in the baseline item, wherein the probability is determined based on;a similarity among the plurality of items based on observable features thereof;
an aggregate online search behavior relative to individual items across multiple users; andpreferences of the user with respect to the baseline item, wherein the baseline item is viewed by the user; and ranking, in decreasing order, probabilities determined for the plurality of items, wherein items that are more likely to be bought by the user are ranked higher than items that are less likely to be bought by the user; determining a probability that the user will select a particular one of the individual items of the plurality of items after the particular baseline item is viewed by the user; and updating the interface running on the computing device, wherein the updated interface presents to the user at least a portion of the plurality of items ranked in decreasing order according to the probabilities; after and in response to the user selecting one of the displayed items, ranking other ones of the portion of the plurality of items and updating the interface running on the computing device to present to the user the other ones of the portion of the plurality of items ranked in decreasing order according to the corresponding probabilities.
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Specification