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A weight-based item recommendation approach for electronic commerce systems

Author

Listed:
  • Ying-Si Zhao

    (Beijing Jiaotong University)

  • Yan-Ping Liu

    (XIDIAN University)

  • Qing-An Zeng

    (North Carolina A&T State University)

Abstract

The recommendation system is a useful tool that can be employed to identify potential relationships between items and users in electronic commerce systems. Consequently, it can remarkably improve the efficiency of a business. The topic of how to enhance the accuracy of a recommendation has attracted much attention by researchers over the past decade. As such, many methods to accomplish this task have been introduced. However, more complex calculations are normally necessary to achieve a higher accuracy, which is not suitable for a real-time system. Hence, in this paper, we propose a weight-based item recommendation approach to provide a balanced formula between the recommended accuracy and the computational complexity. The proposed methods employ a newly defined distance to describe the relationship between the users and the items, after which the recommendations and predictive algorithms are developed. A data analysis based on the MovieLens datasets indicates that the methods applied can obtain suitable prediction accuracy and maintain a relatively low computational complexity.

Suggested Citation

  • Ying-Si Zhao & Yan-Ping Liu & Qing-An Zeng, 2017. "A weight-based item recommendation approach for electronic commerce systems," Electronic Commerce Research, Springer, vol. 17(2), pages 205-226, June.
    • Handle: RePEc:spr:elcore:v:17:y:2017:i:2:d:10.1007_s10660-015-9188-1
    DOI: 10.1007/s10660-015-9188-1
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    References listed on IDEAS

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    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
    2. Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.
    3. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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