Towards Addressing the Coverage Problem in Association Rule-Based Recommender Systems

  • R. Uday Kiran
  • Masaru Kitsuregawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)


Association rule mining is an actively studied topic in recommender systems. A major limitation of an association rule-based recommender system is the problem of reduced coverage. It is generally caused due to the usage of a single global minimum support (minsup) threshold in the mining process, which leads to the effect that no association rules involving rare items can be found. In the literature, Neighborhood-Restricted Rule-Based Recommender System (NRRS) using multiple minsups framework has been proposed to confront the problem. We have observed that NRRS is computationally expensive to use as it relies on an Apriori-like algorithm for generating frequent itemsets. Moreover, it has also been observed that NRRS can recommend uninteresting products to the users. This paper makes an effort to reduce the computational cost and improve the overall performance of NRRS. A pattern-growth algorithm, called MSFP-growth, has been proposed to reduce the computational cost of NRRS. We call the proposed framework as NRRS ⋆ . Experimental results show that NRRS ⋆  is efficient.


Recommender system association rules and coverage problem 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD, pp. 207–216 (1993)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  3. 3.
    Smyth, B., McCarthy, K., Relly, J., O’Sullivan, D., McGinty, L., Wilson, D.C.: Case Studies in Association Rule Mining for Recommender System. In: IC-AI, pp. 809–815 (2005)Google Scholar
  4. 4.
    Lin, W., Alvarez, S.A., Ruiz, C.: Efficient Adaptive-Support Association Rule Mining for Recommender Systems. Data Mining and Knowledge Discovery 6(1), 83–105 (2002)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gedikli, F., Jannach, D.: Neighborhood-restricted mining and weighted application of association rules for recommenders. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 157–165. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: KDD, pp. 337–341 (1999)Google Scholar
  7. 7.
    Hu, Y.-H., Chen, Y.-L.: Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decision Support Systems 42(1), 1–24 (2006)CrossRefGoogle Scholar
  8. 8.
    Uday Kiran, R., Krishna Reddy, P.: An Improved Multiple Minimum Support Based Approach to Mine Rare Association Rules. In: Proceedings of CIDM, pp. 340–347 (2009)Google Scholar
  9. 9.
    Uday Kiran, R., Krishna Reddy, P.: Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. In: Proceedings of EDBT, pp. 11–20 (2011)Google Scholar
  10. 10.
    Uday Kiran, R., Krishna Reddy, P.: An Improved Neighborhood-Restricted Association Rule-based Recommender System Using Relative Support. To be Appeared in ADC (2013)Google Scholar
  11. 11.
    Pei, J., Han, J.: Constrained frequent pattern mining: a pattern-growth view. SIGKDD Explor. Newsl. 4(1), 31–39 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • R. Uday Kiran
    • 1
  • Masaru Kitsuregawa
    • 1
    • 2
  1. 1.Institute of Industrial ScienceThe University of TokyoTokyoJapan
  2. 2.National Institute of InformaticsTokyoJapan

Personalised recommendations