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Method and System for Suggesting Sizes for Fashion Items based on a User’s Purchasing History

IP.com Disclosure Number: IPCOM000234070D
Publication Date: 2014-Jan-09
Document File: 3 page(s) / 196K

Publishing Venue

The IP.com Prior Art Database

Related People

Ken Lai: INVENTOR

Abstract

A method and system is disclosed for suggesting/recommending one or more sizes for one or more fashion items of an e-commerce service based on a user’s purchasing history. The method and system increases the likelihood that the user will choose the right size to buy for a fashion item (e.g., apparel, shoes) based on cross-referencing the user’s purchasing history with others.

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Method and System for Suggesting Sizes for Fashion Items based on a User’s Purchasing History

Abstract

A method and system is disclosed for suggesting/recommending one or more sizes for one or more fashion items of an e-commerce service based on a user’s purchasing history.  The method and system increases the likelihood that the user will choose the right size to buy for a fashion item (e.g., apparel, shoes) based on cross-referencing the user’s purchasing history with others.

Description

Disclosed is a method and system for suggesting/recommending one or more sizes for one or more fashion items of an e-commerce service based on a user’s purchasing history. 

A solution provided by the method and system disclosed herein can be presented to the user at either an item page or checkout flow of the e-commerce service.  When the user is prompted to select a size, a suggested size based on an algorithm is shown to the user.  The size suggestion is only shown if the user is logged in and the method and system deems that a suggestion can be made with sufficient accuracy.

The algorithms for determining the suggested size are as follows:

Basic Algorithm:

When suggesting size for a fashion item C to a user X, the method and system starts by looking at all the items user X has purchased in the same category as C (for example, “shoes”).  The method and system starts by looking at the items user X has bought within the past 1 year.  For example, if the user X has purchased an item A and an item F in the same category as item C previously, and picked size 6 for the size, the method and system, then, starts to look through all users in the database who have purchased item A or item F at size 6 and have also purchased the item C.  For each qualified user, the method and system looks up and stores the size the user picked for the item C.  The method and system, then, calculates the distribution of sizes based on what percent of qualified users have picked a particular size.  If over 80% of qualified users pick a specific size for the item C with referenced count of over 10, the method and system presents that size as a suggestion to the user X interested in the item C.

Fig. 1 illustrates an exemplary implementation of the basic algorithm.

Figure 1

Secondary Algorithm:

When suggesting a size for a fashion item C to user X, the method and system starts by looking at all the items...