SYSTEM AND METHOD FOR COMPUTER-BASED MARKETING
First Claim
1. A method for predicting the reaction of a selected user in a group of users to an item not rated by the selected user in a set of items including items previously rated by the selected user, the method comprising the steps of:
- defining, for each user in the group, and for each item in the set of items sampled by that user, a rating representing the reaction of the user to the item;
defining a plurality of objective archetypes, each representing a hypothetical user and associated with at least one item in the set;
defining, for each of the plurality of objective archetypes, a rating representing the hypothesized reaction of the represented hypothetical user to the associated at least one item;
selecting a set of mentors from the users in the group and from the plurality of objective archetypes based on the similarity of the ratings of each user in the group and each objective archetype to the ratings of the selected user;
successively pairing the selected user with each mentor and computing a similarity function representing the overall rating agreement for the pair;
predicting the rating of the selected user for the not rated item from the similarity functions and the mentors'"'"' ratings of that item
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Abstract
A marketing system and method predicts the interest of a user in specific items—such as movies, books, commercial products, web pages, television programs, articles, push media, etc.—based on that user'"'"'s behavioral or preferential similarities to other users, to objective archetypes formed by assembling items satisfying a search criterion, a market segment profile, a demographic profile or a psychographic profile, to composite archetypes formed by partitioning users into like-minded groups or clusters then merging the attributes of users in a group, or to a combination. The system uses subjective information from users and composite archetypes, and objective information from objective archetypes to form predictions, making the system highly efficient and allowing the system to accommodate “cold start” situations where the preferences of other people are not yet known.
533 Citations
24 Claims
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1. A method for predicting the reaction of a selected user in a group of users to an item not rated by the selected user in a set of items including items previously rated by the selected user, the method comprising the steps of:
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defining, for each user in the group, and for each item in the set of items sampled by that user, a rating representing the reaction of the user to the item;
defining a plurality of objective archetypes, each representing a hypothetical user and associated with at least one item in the set;
defining, for each of the plurality of objective archetypes, a rating representing the hypothesized reaction of the represented hypothetical user to the associated at least one item;
selecting a set of mentors from the users in the group and from the plurality of objective archetypes based on the similarity of the ratings of each user in the group and each objective archetype to the ratings of the selected user;
successively pairing the selected user with each mentor and computing a similarity function representing the overall rating agreement for the pair;
predicting the rating of the selected user for the not rated item from the similarity functions and the mentors'"'"' ratings of that item - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system for predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, comprising:
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a module for defining, for each item sampled by the selected user, a rating representing the reaction of the selected user to that item;
a module for defining a set of raters from the group of users, each rater in the set having a rating for one or more items sampled by the selected user, wherein at least one rater is an objective archetype having hypothetical user ratings for one or more items sampled by the selected user;
a module for successively pairing the selected user with each rater to determine a difference in ratings for items sampled by both members of each successive pair;
a module for designating at least one of the raters as a mentor and assigning a similarity function to the mentor based on the difference in ratings between that mentor and the selected user; and
a module for predicting the reaction of the selected user to the items not yet sampled by the selected user from a prediction function based on the similarity function, the at least one mentor'"'"'s rating of the items, and a previously determined prediction of the selected user'"'"'s reaction to the items.
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13. A method of automatically predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, the reaction predictions being based on other items previously sampled by the selected user, comprising:
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defining, for each item sampled by the selected user, a rating representing the reaction of the selected user to that item;
defining a set of raters including ones of the group of users, each rater in the set having a rating for one or more items sampled by the selected user, wherein at least one rater is an objective archetype having hypothetical user ratings for one or more items sampled by the selected user;
successively pairing the selected user with the raters to determine a difference in ratings for items sampled by both members of each successive pair;
designating at least one of the raters as a mentor and assigning a similarity function based on the difference in ratings between that mentor and the selected user; and
predicting the reaction of the user to the items not sampled by the selected user from a prediction function based on the similarity function, the mentor'"'"'s rating of the items, and a previously determined prediction of the user'"'"'s reaction to the items. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification