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DivRec: A Framework for Top-N Recommendation with Diversification in E-commerce

  • Kejun He
  • Junyu Niu
  • Chaofeng Sha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8709)

Abstract

In order to increase sales for e-commerce websites and meet customer expectations, recommender systems need to recommend more niche products consumers might like. However, traditional product recommender systems usually aim to improve the recommendation accuracy while overlook the diversity within the recommendation lists. In this paper, firstly we examine the importance of diversity within recommended lists through a psychological survey. Motivated by our observations, we develop a general framework, called DivRec, to improve recommendation diversity without lowering accuracy. Experimental results on an e-commerce dataset demonstrate that our approach outperforms state-of-the-art techniques in terms of both accuracy and diversity.

Keywords

Recommender Systems Collaborative Filtering Diversification 

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References

  1. 1.
    Su, X., Khoshgoftaar, T.M.: A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence 2009, Article ID431425S (2009)Google Scholar
  2. 2.
    Agarwal, D., Chen, B.: Regression-based latent factor models. In: ACM SIGKDD 2009, pp. 19–28 (2009)Google Scholar
  3. 3.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. Advances in Neural Information Processing Systems 20, 1257–1264 (2008)Google Scholar
  4. 4.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 145–186 (2011)Google Scholar
  5. 5.
    Drosou, M., Pitoura, E.: Search result diversification. ACM SIGMOD Record 39(1), 41–47 (2010)CrossRefGoogle Scholar
  6. 6.
    Ziegler, C., McNee, S., Konstan, J., Lausen, G.: Improving Recommendation Lists Through Topic Diversification. In: WWW 2005, pp. 22–32 (2005)Google Scholar
  7. 7.
    Yu, C., Lakshmanan, L., Amer-Yahia, S.: It takes variety to make a world: diversification in recommender systems. In: EDBT 2009, pp. 368–378. ACM (2009)Google Scholar
  8. 8.
    Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: ACM SIGIR 2010, pp. 210–217 (2010)Google Scholar
  9. 9.
    Zhao, G., Lee, M.L., Hsu, W., et al.: Increasing temporal diversity with purchase intervals. In: ACM SIGIR 2012, pp. 165–174 (2012)Google Scholar
  10. 10.
    Baumgardner, M., Leippe, M., Ronis, D., Greenwald, A. In: search of reliability persuasion effects: Associative interference and persistence of persuasion in a message-dense environment. Journal of Personality and Social Psychology, 524–537 (1983)Google Scholar
  11. 11.
    Burke, R., Skrull, T.: Competitive Interference and Consumer Memory for Advertising. Journal of Consumer Research, 55–68 (1988)Google Scholar
  12. 12.
    Michael, B.: Proactive Interference and Item Similarity in Working Memory. Journal of Experimental Psychology:Learning, Memory, and Cognition, 183–196 (2006)Google Scholar
  13. 13.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIG 1998, pp. 335–336. ACM (1998)Google Scholar
  14. 14.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems, pp. 30–37. IEEE Computer Society (2009)Google Scholar
  15. 15.
    Kurucz, M., Benczúr, A.A., Csalogány, K.: Methods for large scale SVD with missing values. In: Proceedings of KDD Cup and Workshop, vol. 12, pp. 31–38 (2007)Google Scholar
  16. 16.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys2010, pp. 39–46. ACM (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kejun He
    • 1
  • Junyu Niu
    • 1
  • Chaofeng Sha
    • 1
  1. 1.Software SchoolFudan UniversityShanghaiP.R. China

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