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Research on Commodity Recommendation Algorithm Based on RFN

  • Kai Wang
  • Bohan LiEmail author
  • Shuo Wan
  • Anman Zhang
  • Donghai Guan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

The recommendation system is one of the most widely used applications in E-commerce. By studying the user’s preferences, we can recommend underlying contents for the user from the mass merchandise information. However, most recommendation systems pay much attention on popular products, just ignore those products that are currently not popular but potential for excavation. Our recommendation system based on RFN (Reverse Furthest Neighbor) queries follows the idea of mining popular products in the niche market. We improve the traditional collaborative filtering recommendation algorithm and adopt a collaborative filtering algorithm based on expert users. The modified algorithm can recommend products with potential value based on the power law, which make the distribution of minority mined more adequately by the users. The experimental results show that the recommendation system has high recommendation quality and practical value.

Keywords

Recommendation system Collaborative filtering Reverse furthest neighbor Power law 

References

  1. 1.
    Bobadilla, J., Ortega, F., Hernando, A.: Recommender systems survey. Knowl.-Based Syst. 46(1), 109–132 (2013)CrossRefGoogle Scholar
  2. 2.
    Xu, H.-L., Wu, X., Li, X.-D., Yan, B.-P.: Comparison study of internet recommendation system. J. Softw. 20(2), 350–363 (2009)CrossRefGoogle Scholar
  3. 3.
    Hussein, T.: Context-aware recommender systems. In: ACM Conference on Recommender Systems, pp. 349–350. ACM (2011)Google Scholar
  4. 4.
    Meng, X.W., Xun, H.U., Wang, L.C., et al.: Mobile recommender systems and their applications. J. Softw. 24(1), 91–108 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Anderson, C.: The long tail. Wired Mag. 12(10), 170–177 (2004)Google Scholar
  6. 6.
    Newman, M.E.J.: Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)CrossRefGoogle Scholar
  7. 7.
    Yang, X., Steck, H., Guo, Y., et al.: On top-k recommendation using social networks. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 67–74. ACM (2012)Google Scholar
  8. 8.
    Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. In: Eighth IEEE International Conference on Advanced Learning Technologies, ICALT 2008, pp. 241–245. IEEE (2008)Google Scholar
  9. 9.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on Twitter for personalized news recommendations. In: Konstan, Joseph A., Conejo, R., Marzo, José L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22362-4_1CrossRefGoogle Scholar
  10. 10.
    Yao, B., Li, F., Kumar, P.: Reverse furthest neighbors in spatial databases. In: IEEE International Conference on Data Engineering, pp. 664–675. IEEE (2009)Google Scholar
  11. 11.
    Liu, J., Chen, H., Furuse, K., Kitagawa, H.: An efficient algorithm for arbitrary reverse furthest neighbor queries. In: Sheng, Quan Z., Wang, G., Jensen, Christian S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 60–72. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29253-8_6CrossRefGoogle Scholar
  12. 12.
    Wang, S., Cheema, M.A., Lin, X., et al.: Efficiently computing reverse k furthest neighbors. In: ICDE 2016, pp. 1110–1121 (2016)Google Scholar
  13. 13.
    Goldberg, D., Nichols, D., Oki, B.M., et al.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  14. 14.
    Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  15. 15.
    Zhao, Z.D., Shang, M.S.: User-based collaborative-filtering recommendation algorithms on hadoop. In: International Conference on Knowledge Discovery and Data Mining, pp. 478–481. IEEE (2010)Google Scholar
  16. 16.
    Pirasteh, P., Jung, Jason J., Hwang, D.: Item-based collaborative filtering with attribute correlation: a case study on movie recommendation. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014. LNCS (LNAI), vol. 8398, pp. 245–252. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-05458-2_26CrossRefGoogle Scholar
  17. 17.
    Vozalis, E.G., Konstantinos, G.M.: Recommender systems: an experimental comparison of two filtering algorithms. In: Proceedings of the 9th Panhellenic Conference in Informatics, PCI 2003 (2003)Google Scholar
  18. 18.
    Ma, H., Jia, M., Zhang, D., et al.: Combining tag correlation and user social relation for microblog recommendation. Inf. Sci. Int. J. 385(C), 325–337 (2017)Google Scholar
  19. 19.
    Guo, D., Zhao, H.: Matrix factorization recommendation algorithm fusing tag popularity and time weight. Minicomput. Syst. 37(2), 293–297 (2016)Google Scholar
  20. 20.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: ACM SIGCOMM Computer Communication Review, vol. 29, no. 4, pp. 251–262. ACM (1999)Google Scholar
  21. 21.
    Clementi, F., Gallegati, M.: Power law tails in the Italian personal income distribution. Phys. A: Stat. Mech. Appl. 350(2–4), 427–438 (2005)CrossRefGoogle Scholar
  22. 22.
    Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. SIAM Rev. 51(4), 661–703 (2009)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Zheng, W., Li, B., Wang, Y., Qin, X.: Group recommendation algorithm model combined with preference interaction. Minicomput. Syst. 39(2), 372–378 (2018)Google Scholar
  24. 24.
    Li, B., et al.: Dynamic reverse furthest neighbor querying algorithm of moving objects. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Quan Z. (eds.) ADMA 2016. LNCS (LNAI), vol. 10086, pp. 266–279. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49586-6_18CrossRefGoogle Scholar
  25. 25.
    Zheng, W., et al.: Group recommender model based on preference interaction. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 132–147. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69179-4_10CrossRefGoogle Scholar
  26. 26.
    Yue, L., Chen, W., Li, X., Zuo, W., Yin, M.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 1–47 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kai Wang
    • 1
  • Bohan Li
    • 1
    • 2
    • 3
    Email author
  • Shuo Wan
    • 1
  • Anman Zhang
    • 1
  • Donghai Guan
    • 1
    • 2
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina
  3. 3.Jiangsu Easymap Geographic Information Technology Corp., LtdYangzhouChina

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