首页|A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping

A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping

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In online shopping,most of consumers will not clear their return reasons when submitting return requests (e.g.,select the option "other reasons").Prior literature mostly investigates into the return event at the transaction level,and the underlying force of returns remains untracked.To deal with this problem,we propose a machine learning algorithm named as trust-aware random walk model (TARW).In the proposed model,four patterns of consumers can be identified in terms of return forces:(i) selfish consumers,(ii) honest consumers,(iii) fraud consumers,and (iv) irrelevant consumers.To profile consumers' return patterns,we capture consumers' similarities in order preferences and return tendencies separately.Based on consumers' similarities,we obtain a return pattern trust network by introducing the trust network and collaborative filtering algorithms.Subsequently,we develop two important applications based on the trust network:(i) estimating consumers' return propensities for product types;(ii) scoring the anomaly for consumers' returns for one product.Finally,we conduct extensive experiments with the real-world data to validate the model's effectiveness in predicting and tracing consumers' returns.With the proposed model,we can help retailers improve the conversion rates of selfish consumers,retain honest consumers,and block fraud consumers.

machine learningreturn abuserandom walkcollaborative filteringreturn pattern

Xiaolin LI、Yuan ZHUANG、Yanjie FU、Xiangdong HE

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School of Business, Nanjing University, Nanjing 210093, China

Department of Computer Science, Missouri University of Science and Technology, Rolla MO 65401, USA

Network and Information Center, Nanjing University, Nanjing 210093, China

This work was supported by National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaPhilosophy and Social Science Foundation of Higher Education Institutions of Jiangsu Province,China

Grant No.2018YFB-1004300Grant Nos.6177319971732002Grant No.2017SJB0006

2019

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSCDSCIEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2019.62(5)
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