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Evidence-driven dubious decision making in online shopping

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Abstract

Nowadays, Online shopping has been tremendous lifestyle choices due to the lower management cost for product/service providers and the cheaper prices for buyers/customers. Meanwhile, it raises a big challenge for both buyers and sellers to identify the right product items from the numerous choices and the right customers from a large number of different buyers. This motivates the study of recommendation system which computes recommendation scores for product items and filters out those with low scores. Recently, a promising direction involves the consideration of the social network influence in recommendation system. While significant performance improvement has been observed, it is still unclear to which extension the social network influence can help differentiate product items in terms of recommendation scores. This is an interesting problem in particular in the situation that the recommended product items have the highly similar (or identical) scores. As the first effort to this problem, this paper probes the boundary of social network influence to recommendation outputs by solving an optimization problem called evidence-driven dubious decision making. Two solutions have been proposed and the evaluation on two real world datasets has verified the effectiveness of the proposed solutions.

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Notes

  1. http://www.trustlet.org/downloaded_epinions.html

  2. https://www.librec.net/datasets.html#filmtrust

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Acknowledgements

This work was partially supported by the ARC Discovery Projects under Grant No. DP160102114 and DP160102412.

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, China

    Qiao Tian

  2. Department of Computer Science and Software Engineering, University of Western Australia, Perth, Australia

    Jianxin Li & Mark Reynolds

  3. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia

    Lu Chen & Chengfei Liu

  4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Rong-hua Li

  5. School of Science, RMIT University, Melbourne, Australia

    Ke Deng

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  1. Qiao Tian
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  2. Jianxin Li
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  5. Rong-hua Li
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Correspondence to Jianxin Li.

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This article belongs to the Topical Collection: Special Issue on Social Computing and Big Data Applications

Guest Editors: Xiaoming Fu, Hong Huang, Gareth Tyson, Lu Zheng, and Gang Wang

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Tian, Q., Li, J., Chen, L. et al. Evidence-driven dubious decision making in online shopping. World Wide Web 22, 2883–2899 (2019). https://doi.org/10.1007/s11280-018-0618-6

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