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


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.

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This work was supported by National Key R&D Program of China (Grant No. 2018YFB-1004300), National Natural Science Foundation of China (Grant Nos. 61773199, 71732002), and Philosophy and Social Science Foundation of Higher Education Institutions of Jiangsu Province, China (Grant No. 2017SJB0006).

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Correspondence to Yanjie Fu or Xiangdong He.

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Li, X., Zhuang, Y., Fu, Y. et al. A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping. Sci. China Inf. Sci. 62, 52101 (2019). https://doi.org/10.1007/s11432-018-9511-1

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  • machine learning
  • return abuse
  • random walk
  • collaborative filtering
  • return pattern