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A weight-based item recommendation approach for electronic commerce systems

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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.

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References

  1. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

    Article  Google Scholar 

  2. Mooney, R. J., & Roy, L. (2000). Content-based book recommending using learning for text categorization. In Proceedings of the fifth ACM conference on digital libraries, San Antonio, Texas, June (pp. 195–204).

  3. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. IEEE Computer, 42(8), 30–37.

    Article  Google Scholar 

  4. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Proceedings of Communication of the ACM, 35(12), 61–70.

    Article  Google Scholar 

  5. Bell, R. M., & Koren, Y. (2007, August). Improved neighborhood-based collaborative filtering. In KDD cup and workshop at the 13th ACM SIGKDD international conference on knowledge discovery and data mining.

  6. Nageswara Rao, K., & Talwar, V. G. (2008). Application domain and functional classification of recommender systems a survey. DESIDOC Journal of Library & Information Technology, 28(3), 17–35.

    Article  Google Scholar 

  7. Su, X.-Y., & Khoshgoftaar, T.,M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 421–425. doi: 10.1155/2009/421425.

  8. Shani, G., Heckerman, D., & Brafman, R. I. (2005). An MDP-based recommender system. The Journal of Machine Learning Research, 6, 1265–1295.

    Google Scholar 

  9. Pennock, D.M., Horvitz, E., Lawrence S., & Giles, C.L. (2000). Collaborative filtering by personality diagnosis: A hybrid memory- and model-based approach. In Proceedings of the 16th conference on uncertainty in artificial intelligence (UAI ‘00): (pp.473–480).

  10. Balabanovic, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Proceedings of Communication of the ACM, 40(3), 66–72.

    Article  Google Scholar 

  11. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, L. (1997). GroupLens: Applying collaborative filtering to usenet news. Proceedings of Communications of the ACM, 40(3), 77–87.

    Article  Google Scholar 

  12. Resnick, P., Iacovou, N., & Suchak, M., et al. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM conference on computer-supported cooperative work (pp. 175–186).

  13. Deshpande, M., & Karypis, G. (2004). Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22(1), 143–177.

    Article  Google Scholar 

  14. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th conference on uncertainly in artificial intelligence (pp. 43–52).

  15. Herlocker, J. L., Konstan, J. A., & Borchers, A., et al. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the 1999 conference on research and development in information retrieval (pp. 230–237).

  16. Chang, Y. I., Shen, J. H., & Chen, T. I. (2008). A data mining-based method for the incremental update of supporting personalized information filtering. Information Science and Engineering, 24(1), 129–142.

    Google Scholar 

  17. Kim, H. L., Decker, S. & Breslin, J. G. (2010). Representing and sharing folksonomies with semantics. Journal of Information Science, 36(1), 57–72.

  18. Palau, J., Montaner, M., & Lopez, B., et al. (2004). Collaboration analysis in recommender systems using social networks. Cooperative Information Agents VIII (pp.137–151) Berlin: Springer.

  19. Kunaver, M., Požrl, T., Pogacnik, M., & Tasic, J. (2007). Optimization of combined collaborative recommender systems. International Journal of Electronics and Communications (AEÜ), 61, 433–443.

    Article  Google Scholar 

  20. Mohammad, Y. H., & Kamal, K. B. (2008). Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Systems with Applications, 35(3), 1386–1399.

    Article  Google Scholar 

  21. Hand, D., & Smyth, H. (2001). Principles of data mining. Cambridge: The MIT Press.

    Google Scholar 

  22. Freund, Y., Iyer, R., Schapire, R. E., et al. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 933–969.

    Google Scholar 

  23. Pavlov, D. Y., & Pennock, D. (2002). A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains. In Proceedings of the 16th annual conference of neural information processing systems (pp. 1441–1448).

  24. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285–295).

  25. Ungar, L. H., & Foster, D. P. (1998). Clustering methods for collaborative filtering. In Proceedings of workshop of technical report, Menlo Park (pp. 114–128).

  26. Marlin, B. (2003). Modeling user rating profiles for collaborative filtering. In Proceeding of the 16th annual conference of neural information processing systems.

  27. O’Connor, M., & Herlocker, J. (2011). Clustering items for collaborative filtering. Retrieved October 15, 2011, from http://www.cs.umbc.edu/~ian/sigir99-rec/papers/oco.

  28. Liu, C., & Zhou, W.-X. (2012). Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm. Physica A: Statistical Mechanics and Its Applications, 391(22), 5704–5711.

    Article  Google Scholar 

  29. Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8), 3879–3900.

    Article  Google Scholar 

  30. Song, R. P., Wang, B., Huang, G. M., Liu, Q. D., Hu, R. J., & Zhang, R. S. (2013). A hybrid recommender Algorithm based on an improved similarity method. Applied Mechanics and Materials, 475–476, 978–982.

    Article  Google Scholar 

  31. Robert, M. B., & Yehuda, K. (2007). Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Proceedings of seventh IEEE international conference on data mining.

  32. Vozalis, M. G., & Margaritis, K. G. (2007). Using SVD and demographic data for the enhancement of generalized collaborative filtering. Information Sciences, 177, 3017–3037.

    Article  Google Scholar 

  33. Kumar, R., Verma, B. K., & Rastogi, S. S. (2014). Social popularity based SVD++ recommender system. International Journal of Computer Applications, 87(14), 33–37.

    Article  Google Scholar 

  34. Koren, Y. (2008). Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of KDD’08.

  35. Jahrer, M., Toscher, A., & Legenstein, R. (2010). Combining predictions for accurate recommender systems. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 693–702). New York: ACM.

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Acknowledgments

The work was supported by the Fundamental Research Funds for the Central Universities (No.B15JB00220).

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Authors and Affiliations

  1. School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China

    Ying-Si Zhao

  2. XIDIAN University, Xi’an, 710071, China

    Yan-Ping Liu

  3. School of Technology, North Carolina A&T State University, Greensboro, NC, 27411, USA

    Qing-An Zeng

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  1. Ying-Si Zhao
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  2. Yan-Ping Liu
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  3. Qing-An Zeng
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Correspondence to Ying-Si Zhao.

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Zhao, YS., Liu, YP. & Zeng, QA. A weight-based item recommendation approach for electronic commerce systems. Electron Commer Res 17, 205–226 (2017). https://doi.org/10.1007/s10660-015-9188-1

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  • DOI: https://doi.org/10.1007/s10660-015-9188-1

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