Alleviating Sparsity and Scalability Issues in Collaborative Filtering Based Recommender Systems

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)


Commercial recommender systems in general are used to evaluate very large product sets. In a user – item rating database, though users are very active, there are a few rating of the total number of items available. The user-item matrix is thus extremely sparse. Since a collaborative filtering algorithm is mainly based on similarity measures computed over the co-rated set of items, the large levels of sparsity can lead to less accuracy and can challenge the predictions or recommendations of the collaborative filtering (CF)systems. Further, a CF algorithm is assumed to be efficient if it is able to filter items that are interesting to users. But, they require computations that are very expensive and grow non-linearly with the number of users and items in a database. In general, the whole ratings database is searched in collaborative filtering and thus it suffers from poor scalability when more and more users and items are added into the database. Instigated by these challenges, we investigate two collaborative filtering algorithms, firstly an algorithm based on weighted slope one scheme and item clustering & secondly an algorithm based on item classification & item clustering, which deal with the sparsity and scalability issues simultaneously. Experiments were carried to determine which is better in terms of simplicity and accuracy among the two methods.


Recommender System Collaborative Filtering Sparsity Scalability 


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  1. 1.
    Carlson, C.N.: Information overload, retrieval strategies and Internet user empowerment. In: Proceedings of the Good, the Bad and the Irrelevant, pp. 169–173. University of Art and Design, Helsinki (2003)Google Scholar
  2. 2.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of the ACM Conference on Human Factors in Computing System, pp. 194–201 (1995)Google Scholar
  3. 3.
    Resnick, P., Varian, H.R.: Recommender Systems. Guest Editor’s Introduction to the Special Section. Communications of the ACM 40(3), 56–58 (1997)Google Scholar
  4. 4.
    Terveen, L.G., Hill, W.: Beyond Recommender Systems: Helping People Help Each Other. In: Carroll, J. (ed.) HCI in the New Millennium. Addison Wesley (2001)Google Scholar
  5. 5.
    Resnick, P., Iakovou, N., Sushak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of netnews. In: Proceedings of the Computer Supported Cooperative Work Conference (1994)Google Scholar
  6. 6.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating ‘word of mouth’. In: Proceedings of the Conference on Human Factors in Computing Systems (1995)Google Scholar
  7. 7.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  8. 8.
    Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. In: Resnick, Varian (eds.), pp. 66–72Google Scholar
  9. 9.
    Lee, W.S.: Collaborative learning for recommender systems. In: Proceedings of the International Conference on Machine Learning (2001)Google Scholar
  10. 10.
    Schafer, J.B., Konston, J.A., Riedl, J.: Recommender systems in e-Commerce. In: Proceedings of ACM Conference on e-commerce, pp. 158–166 (1999)Google Scholar
  11. 11.
    Linden, G., Smith, B., York, J.: Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80 (2003)CrossRefGoogle Scholar
  12. 12.
    Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.: MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 263–268 (2003)Google Scholar
  13. 13.
    Good, N., Schafer, J.B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: Proceedings of American Association of Artificial Intelligence, pp. 439–446 (1999)Google Scholar
  14. 14.
    Lemire, D., Maclachlan, A.: Slope One Predictors for Online Rating-Based Collaborative Filtering. In: SIAM Data Mining (2005)Google Scholar
  15. 15.
    Wang, P., Ye, H.W.: A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering. In: Proceedings of the 2009 International Conference on Industrial and Information Systems, IIS 2009 (2009)Google Scholar
  16. 16.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson EducationGoogle Scholar
  17. 17.
    Gong, S.J.: A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item clustering. Journal of Software 5(7), 745–752 (2010)CrossRefGoogle Scholar
  18. 18.

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dept. of Computer EngineeringDelhi Technological UniversityDelhiIndia

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