US 2019/0156354 A1 · SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR PREDICTING ITEM PREFERENCE USING REVENUE-WEIGHTED COLLABORATIVE FILTER

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.

Claims20

Dependent claims

A method for identifying consumer items more likely to be bought by a user, comprising:

determining, by a computer having a processor and a memory, 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 a 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 across multiple users;
and
preferences of the user with respect to the baseline item, wherein the baseline item is established 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.

4 more elements

A computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by at least one processor to perform:

determining, for each individual item of a plurality of items, a probability that a user will select the individual item, given that the user has expressed interest in a 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 across multiple users;
and
preferences of the with respect to the baseline item, wherein the baseline item is established 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.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application

16/240,339Filed 4 Jan 2019

Priority

30 Jun 2011

Published

23 May 2019

Term ends

Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06Q 30/0201
Marketing; Price estimation or determination; Fundraising: market modelling; Market analysis; Collecting market data
G06F 16/24578
With adaptation to user needs: using ranking
G06F 16/9535
Querying, e.g. by the use of web search engines: search customisation based on user profiles and personalisation
G06F 16/9536
Querying, e.g. by the use of web search engines: search customisation based on social or collaborative filtering

Family

Application13/173,332Continuation · filed 30 Jun 2011

Application14/152,884Continuation · filed 10 Jan 2014

Application15/297,883Continuation · filed 19 Oct 2016

Application16/240,339Continuation

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