US 8,478,663 B2 · Fit recommendation via collaborative inference

Abstract

Some embodiments of the invention provide techniques for recommending a size of a subject item to fit a subject consumer. In some embodiments, clusters of consumers with fit characteristics similar to the subject consumer are identified, using one or more data clustering algorithms, based on any of numerous consumer attributes (e.g., self-reported and/or inferred height, weight, body shape, body characteristics, and/or purchase histories (e.g., consumers with high overlap in terms of sets of products purchased)). Information on other consumers in the cluster may be analyzed to draw conclusions on how different sizes of the subject item may fit the subject consumer. For example, the purchase history of other members of the cluster may be analyzed to determine whether other members purchased a particular size of the item, and if so, the size purchased by the other members may serve as a basis to recommend a size that may best fit the consumer. For example, if other members of the cluster purchased a particular size, then that size may be recommended to the subject consumer, or if other members of the cluster purchased and then returned a particular size (e.g., for being too small), then another (e.g., larger) size may be recommended to the subject consumer.

Claims19

Dependent claims

A method of employing at least one computer to generate a recommendation, for a subject consumer, of a size of a subject item for which a plurality of sizes is available, the method comprising:

(A) receiving at the at least one computer a request for a recommendation of a size of the subject item to fit the subject consumer;
(B) using the at least one computer, identifying a cluster of consumers, comprising a plurality of consumers, to which the subject consumer belongs, and analyzing information relating to the cluster of consumers;
(C) determining, by the at least one computer, whether a recommendation of a size of the subject item is to be made as a result of the analyzing in (B);
(D) if it is determined in (C) that a recommendation of a size of the subject item is not to be made as a result of the analyzing in (B), identifying, by the at least one computer, a cluster of items, comprising a plurality of items, to which the subject item belongs, and analyzing information relating to the cluster of items;
(E) determining, by the at least one computer, whether a recommendation of a size of the subject item is to be made as a result of the analyzing in (D);
and
(F) if it is determined in (C) or in (E) that a recommendation of a size of the subject item is to be made, using the at least one computer to generate a recommendation for a size of the subject item as being likely to fit the subject consumer.

4 more elements

At least one non-transitory computer readable medium having encoded thereon instructions which, when executed, perform a method of recommending, to a subject consumer, a size of a subject item for which a plurality of sizes is available, the method comprising:

(A) receiving a request for a recommendation of a size of the subject item to fit the subject consumer;
(B) identifying a cluster of consumers, comprising a plurality of consumers, to which the subject consumer belongs, and analyzing information relating to the cluster of consumers;
(C) determining whether a recommendation of a size of the subject item is to be made as a result of the analyzing in (B);
(D) if it is determined in (C) that a recommendation of a size of the subject item is not to be made as a result of the analyzing in (B), identifying a cluster of items, comprising a plurality of items, to which the subject item belongs, and analyzing information relating to the cluster of items;
(E) determining whether a recommendation of a size of the subject item is to be made as a result of the analyzing in (D);
and
(F) if it is determined in (C) or in (E) that a recommendation of a size of the subject item is to be made, recommending a size of the subject item as being likely to fit the subject consumer.

4 more elements

Dependencies are inferred from the claim text.

Fit recommendation via collaborative inference

Life of this patent

Fit recommendation via collaborative inference

Life of this patent

Abstract

Claims19