Effective Next-Items Recommendation via Personalized Sequential Pattern Mining

  • Ghim-Eng Yap
  • Xiao-Li Li
  • Philip S. Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7239)


Based on the intuition that frequent patterns can be used to predict the next few items that users would want to access, sequential pattern mining-based next-items recommendation algorithms have performed well in empirical studies including online product recommendation. However, most current methods do not perform personalized sequential pattern mining, and this seriously limits their capability to recommend the best next-items to each specific target user. In this paper, we introduce a personalized sequential pattern mining-based recommendation framework. Using a novel Competence Score measure, the proposed framework effectively learns user-specific sequence importance knowledge, and exploits this additional knowledge for accurate personalized recommendation. Experimental results on real-world datasets demonstrate that the proposed framework effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified frequent patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users.


Recommender System Sequential Pattern Frequent Pattern Pattern Mining Cosine Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ghim-Eng Yap
    • 1
  • Xiao-Li Li
    • 1
  • Philip S. Yu
    • 2
  1. 1.Institute for Infocomm ResearchSingapore
  2. 2.Department of Computer ScienceUniversity of IllinoisChicagoUSA

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