Advertisement

Improved Slope One Collaborative Filtering Predictor Using Fuzzy Clustering

  • Tianyi Liang
  • Jiancong Fan
  • Jianli Zhao
  • Yongquan Liang
  • Yujun Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

Slope One predictor, an item-based collaborative filtering algorithm, is widely deployed in real-world recommender systems because of its conciseness, high-efficiency and reasonable accuracy. However, Slope One predictor still suffers two fundamental problems of collaborative filtering : sparsity and scalability, and its accuracy is not very competitive. In this paper, to alleviate the sparsity problem for Slope One predictor, and boost its scalability and accuracy, an improved algorithm is proposed. Through fuzzy clustering technique, the proposed algorithm captures the latent information of users thereby improves its accuracy, and the clustering mechanism makes it more scalable. Additionally, a high-accuracy filling algorithm is developed as preprocessing tool to tackle the sparsity problem. Finally empirical studies on MovieLens and Baidu dataset support our theory.

Keywords

Slope One fuzzy clustering collaborative filtering sparsity scalability 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. J. Society for Industrial Mathematics 5, 471–480 (2005)Google Scholar
  2. 2.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. J. Advances in Artificial Intelligence, 4 (2009)Google Scholar
  3. 3.
    Cacheda, F., Carneiro, V., Fernndez, D., et al.: Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. J. ACM Transactions on the Web (TWEB) 5(1), 2 (2011)Google Scholar
  4. 4.
    Koren, Y., Bell, R.: Advances in collaborative filtering: Recommender Systems Handbook, pp. 145–186. M. Springer US (2011)Google Scholar
  5. 5.
    Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques. M. Morgan kaufmann (2012)Google Scholar
  6. 6.
    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
  7. 7.
    Xue, G.R., Lin, C., Yang, Q., et al.: 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.
    Sarwar, B., Karypis, G., Konstan, J., et al.: Application of dimensionality reduction in recommender system-a case study. Minnesota Univ. Minneapolis Dept. of Computer Science (2000)Google Scholar
  9. 9.
    Ma, C.-C.: A Guide to Singular Value Decomposition for Collaborative Filtering (2008)Google Scholar
  10. 10.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)Google Scholar
  11. 11.
    Wu, K.W., Ferng, C.S., Ho, C.H., et al.: A two-stage ensemble of diverse models for advertisement ranking. In: KDD Cup 2012 ACM SIGKDD KDD-Cup WorkShop (2012)Google Scholar
  12. 12.
    Balabanovi, M., Shoham, Y.: Fab: content-based, collaborative recommendation. J. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  13. 13.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Gao, M., Wu, Z.: Personalized context-aware collaborative filtering based on neural network and slope one. In: Luo, Y. (ed.) CDVE 2009. LNCS, vol. 5738, pp. 109–116. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Wu, J., Li, T.: A modified fuzzy C-means algorithm for collaborative filtering. In: Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition. ACM (2012)Google Scholar
  16. 16.
    The download link of Baidu contest dataset, http://pan.baidu.com/share/link?shareid=340221&uk=2000006609

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tianyi Liang
    • 1
  • Jiancong Fan
    • 1
  • Jianli Zhao
    • 1
  • Yongquan Liang
    • 1
  • Yujun Li
    • 2
  1. 1.School of Information Science and EngineeringShandong University of Science and TechnologyQingdaoChina
  2. 2.Hisense State Key Laboratory of Digital Multi-media TechnologyChina

Personalised recommendations