E-tail Product Return Prediction Via Hypergraph-based Local Graph Cut

Authors:
Jianbo Li Three Bridges Capital
Jingrui He Arizona State University
Yada Zhu IBM

Introduction:

This paper studies the problems of E-tail, which has provided customers with great convenience by allowing them to purchase retail products anywhere without visiting the actual stores.

Abstract:

Recent decades have witnessed the rapid growth of E-commerce. In particular, E-tail has provided customers with great convenience by allowing them to purchase retail products anywhere without visiting the actual stores. A recent trend in E-tail is to allow free shipping and hassle-free returns to further attract online customers. However, a downside of such a customer-friendly policy is the rapidly increasing return rate as well as the associated costs of handling returned online orders. Therefore, it has become imperative to take proactive measures for reducing the return rate and the associated cost. Despite the large amount of data available from historical purchase and return records, up until now, the problem of E-tail product return prediction has not attracted much attention from the data mining community.

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