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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of IEEE International Conference on Data Engineering (ICDE 1995), pp. 3–14 (1995)Google Scholar
  2. 2.
    Asuncion, A., Newman, D.: UCI ML Repository (2007), http://www.ics.uci.edu/~mlearn
  3. 3.
    Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential pattern mining using a bitmap representation. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 429–435 (2002)Google Scholar
  4. 4.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 331–370 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Capelle, M., Masson, C., Boulicaut, J.-F.: Mining Frequent Sequential Patterns under a Similarity Constraint. In: Yin, H., Allinson, N.M., Freeman, R., Keane, J.A., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, pp. 1–6. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Chang, J.H.: Mining weighted sequential patterns in a sequence database with a time-interval weight. Knowledge-Based Systems (2010) Available OnlineGoogle Scholar
  7. 7.
    Chen, E., Cao, H., Li, Q., Qian, T.: Efficient strategies for tough aggregate constraint-based sequential pattern mining. Information Sciences 178(6), 1498–1518 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Cheng, H., Yan, X., Han, J.: IncSpan: Incremental mining of sequential patterns in large databases. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 527–532 (2004)Google Scholar
  9. 9.
    Cheng, H., Yan, X., Han, J., Hsu, C.-W.: Discriminative Frequent Pattern Analysis for Effective Classification. In: Proceedings of IEEE International Conference on Data Engineering (ICDE 2007), pp. 716–725 (April 2007)Google Scholar
  10. 10.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of ACM International Conference on Web Search and Data Mining (WSDM 2010), pp. 241–250 (2010)Google Scholar
  11. 11.
    Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., et al.: FreeSpan: Frequent pattern-projected sequential pattern mining. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), pp. 355–359 (2000)Google Scholar
  12. 12.
    Huang, C.-L., Huang, W.-L.: Handling sequential pattern decay: Developing a two-stage collaborative recommender system. ECRA 8(3), 117–129 (2009)Google Scholar
  13. 13.
    Huang, J.-W., Tseng, C.-Y., Ou, J.-C., Chen, M.-S.: A general model for sequential pattern mining with a progressive database. IEEE TKDE 20(9), 1153–1167 (2008)Google Scholar
  14. 14.
    Kum, H.-C., Pei, J., Wang, W., Duncan, D.: ApproxMAP: Approximate mining of consensus sequential patterns. In: Proceedings of SIAM Intl. Conf. on Data Mining, pp. 311–315 (2003)Google Scholar
  15. 15.
    Li, C., Lu, Y.: Similarity measurement of web sessions by sequence alignment. In: Procs. of IFIP Intl. Conf. on Network and Parallel Comp. Workshops (NPC 2007), pp. 716–720 (2007)Google Scholar
  16. 16.
    Lin, M.-Y., Hsueh, S.-C., Chang, C.-W.: Fast discovery of sequential patterns in large databases using effective time-indexing. Information Sciences 178(22), 4228–4245 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Lo, S.: Binary prediction based on weighted sequential mining method. In: Proceedings of International Conference on Web Intelligence (WI 2005), pp. 755–761 (2005)Google Scholar
  18. 18.
    Mitchell, T.: Machine Learning. McGraw Hill (1997)Google Scholar
  19. 19.
    Onuma, K., Tong, H., Faloutsos, C.: TANGENT: A novel, ’surprise me’, recommendation algorithm. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 657–665 (2009)Google Scholar
  20. 20.
    Pei, J., Han, J., Mortazavi-Asl, B., et al.: Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE TKDE 16(11), 1424–1440 (2004)Google Scholar
  21. 21.
    Pei, J., Han, J., Wang, W.: Mining sequential patterns with constraints in large databases. In: Procs. of ACM Intl. Conf. on Info. and Knowl. Management (CIKM 2002), pp. 18–25 (2002)Google Scholar
  22. 22.
    Pei, J., Fu, A.W.-C., Lin, X., Wang, H.: Computing compressed multidimensional skyline cubes efficiently. In: Procs. of IEEE Intl. Conf. on Data Eng. (ICDE 2007), pp. 96–105 (2007)Google Scholar
  23. 23.
    Pyo, S., Kim, E., Kim, M.: Automatic recommendation of (IP)TV program schedules using sequential pattern mining. In: Adjunct Proceedings of EuroITV 2009, pp. 50–53 (2009)Google Scholar
  24. 24.
    Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of International Conference on World Wide Web (WWW 2010), pp. 811–820 (2010)Google Scholar
  25. 25.
    Resnick, P., Varian, H.R.: Recommender Systems. Comms. of the ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  26. 26.
    Salton, G.: Automatic text processing: The transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing (1989)Google Scholar
  27. 27.
    Saneifar, H., Bringay, S., Laurent, A., Teisseire, M.: S2MP: Similarity measure for sequential patterns. In: Procs. of Australasian Data Mining Conf. (AusDM 2008), pp. 95–104 (2008)Google Scholar
  28. 28.
    Schifanella, R., Barrat, A., Cattuto, C., et al.: Folks in folksonomies: Social link prediction from shared metadata. In: Proceedings of ACM International Conference on Web Search and Data Mining (WSDM 2010), pp. 271–280 (2010)Google Scholar
  29. 29.
    Sequeira, K., Zaki, M.: Admit: Anomaly-based data mining for intrusions. In: Proceedings of ACM Intl. Conf. on Knowledge Discovery and Data Mining (KDD 2002), pp. 386–395 (2002)Google Scholar
  30. 30.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  31. 31.
    Torrey, L., Shavlik, J.: Transfer Learning. In: Handbook of Research on Machine Learning Applications. IGI Global (2009)Google Scholar
  32. 32.
    Yun, U.: A new framework for detecting weighted sequential patterns in large seq. databases. Knowledge-Based Systems 21, 110–122 (2008)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42(1/2), 31–60 (2001)zbMATHCrossRefGoogle Scholar
  34. 34.
    Zhou, B., Hui, S.C., Chang, K.: An intelligent recommender system using sequential web access patterns. In: Procs. of IEEE Conf. on Cybernetics and Intell. Sys., pp. 393–398 (2004)Google Scholar
  35. 35.
    Zobel, J., Moffat, A.: Inverted files for text search engine. ACM Comp. Surveys 38(2) (2006)Google Scholar

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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