Multi-feature Collaborative Filtering Recommendation for Sparse Dataset

  • Zengda GuanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)


Collaborative filtering algorithms become losing its effectiveness on case that the dataset is sparse. When user ratings are scared, it’s difficult to find real similar users, which causes performance reduction of the algorithm. We here present a 3-dimension collaborative filtering framework which can use features of users and items for similarity computation to deal with the data sparsity problem. It uses feature and rating combinations instead of only ratings in collaborative filtering process and performs a more complete similarity computation. Specifically, we provide a weighted feature form and a Bayesian form in its implementation. The results demonstrate that our methods can obviously improve the performance of collaborative filtering when datasets are sparse.


Collaborative filtering Sparse dataset Multi-feature similarity 



The author gratefully acknowledges the generous support from the Doctoral Fund of Shandong Jianzhu University (XNBS1527).


  1. 1.
    Terveen, L., Hill, W.: Beyond recommender systems: helping people help each other. In: HCI in the New Millennium, p. 6. Addison-Wesley (2001)Google Scholar
  2. 2.
    Linden, G., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  3. 3.
    Su, X., Khoshgoftaar, T.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 19 (2009)Google Scholar
  4. 4.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work (1994)Google Scholar
  5. 5.
    Konstan, T., Miller, B., Maltz, D., Herlacker, J., Gordon, L., Riedl, J.: GroupLens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  6. 6.
    Delgado, J.: Agent-based information filtering and recommender systems on the Internet. Ph.D. thesis, Nagoya Institute of Technology (2000)Google Scholar
  7. 7.
    Xue, G., Lin, C., Yang, Q., Xi, W., Zeng, H., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 114–121. ACM (2005)Google Scholar
  8. 8.
    Li, B., Yang, Q., Xue, X.: Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In: IJCAI-09, vol. 9, pp. 2052–2057 (2009)Google Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 230–237 (1999)Google Scholar
  10. 10.
    Chee, S.H.S., Han, J., Wang, K.: RecTree: an efficient collaborative filtering method. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 141–151. Springer, Heidelberg (2001). Scholar
  11. 11.
    Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998) (1998)Google Scholar
  12. 12.
    Su, X., Khoshgoftaar, T.M., Greiner, R.: A mixture imputation-boosted collaborative filter. In: Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2008), Coconut Grove, Fla, USA, pp. 312–317, May 2008Google Scholar
  13. 13.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001)CrossRefGoogle Scholar
  14. 14.
    Fisher, D., Hildrum, K., Hong, J., Newman, M., Thomas, M., and Vuduc, R.: SWAMI: a framework for collaborative filtering algorithm development and evaluation. In: Proceedings of the 23rd Annual International Conference on Researech and Development in Information Retrieval (SIGIR) (2000)Google Scholar
  15. 15.
    Najafabadi, M.K., Mahrin, M.N.R., Chuprat, S., Sarkan, H.M.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput. in Hum. Behav. 67, 113–128 (2017)CrossRefGoogle Scholar
  16. 16.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content boosted collaborative filtering for improved recommendations. In: Proceedings of the 18th National Conference on Artificial Intelligence (AAAI 2002), Edmonton, Canada, pp. 187–192 (2002)Google Scholar
  17. 17.
    Greinemr, R., Su, X., Shen, B., Zhou, W.: Structural extension to logistic regression: discriminative parameter learning of belief net classifiers. Mach. Learn. 59(3), 297–322 (2005)CrossRefGoogle Scholar
  18. 18.
    Yahoo! Webscope movie data set.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business SchoolShandong Jianzhu UniversityJinanChina

Personalised recommendations