Incremental Set Recommendation Based on Class Differences

  • Yasuyuki Shirai
  • Koji Tsuruma
  • Yuko Sakurai
  • Satoshi Oyama
  • Shin-ichi Minato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)


In this paper, we present a set recommendation framework that proposes sets of items, whereas conventional recommendation methods recommend each item independently. Our new approach to the set recommendation framework can propose sets of items on the basis on the user’s initially chosen set. In this approach, items are added to or deleted from the initial set so that the modified set matches the target classification. Since the data sets created by the latest applications can be quite large, we use ZDD (Zero-suppressed Binary Decision Diagram) to make the searching more efficient. This framework is applicable to a wide range of applications such as advertising on the Internet and healthy life advice based on personal lifelog data.


recommendation classification collaborative filtering zero-suppressed binary decision diagram 


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  1. 1.
    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
  2. 2.
    Bryant, R.E.: Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computers 35(8) (August 1986)Google Scholar
  3. 3.
    Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by Aggregating Emerging Patterns. In: Arikawa, S., Nakata, I. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  4. 4.
    Knuth, D.E.: The Art of Computer Programming. Bitwise Tricks & Techniques, vol. 4(1), pp. 117–126. Addison-Wesley (2009)Google Scholar
  5. 5.
    Melville, P., Sindhwani, V.: Recommender Systems. In: Encyclopedia of Machine Learning, pp. 829–838. Springer (2010)Google Scholar
  6. 6.
    Minato, S.: Zero-Suppressed BDDs for Set Manipulation in Combinatorial Problems. In: Proc. of 30th ACM/IEEE Design Automation Conference, DAC 1993 (1993)Google Scholar
  7. 7.
    Minato, S.: VSOP (Valued-Sum-of-Products) Calculator for Knowledge Processing Based on Zero-Suppressed BDDs. In: Jantke, K.P., Lunzer, A., Spyratos, N., Tanaka, Y. (eds.) Federation over the Web. LNCS (LNAI), vol. 3847, pp. 40–58. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Minato, S.: Implicit Manipulation of Polynomials Using Zero-Suppressed BDDs. In: Proc. of IEEE The European Design and Test Conference (1995)Google Scholar
  9. 9.
    Navarro, G.: A Guided Tour to Approximate String Matching. ACM Computing Surveys 33(1) (2001)Google Scholar
  10. 10.
    Parameswaran, A., Venetis, P., Garcia-Molina, H.: Recommendation Systems with Complex Constraints: A Course Recommendation Perspective. Transactions on Information Systems 29(4) (2011)Google Scholar
  11. 11.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence (2009)Google Scholar
  12. 12.
    Xie, M., Lakshmanan, L.V.S., Wood, P.T.: Breaking out of the box of recommendations: From Items to Packages. In: Proc. of the 4th ACM Conf. on Recommender Systems (2010)Google Scholar
  13. 13.
    (Rakuten data disclosure),
  14. 14.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yasuyuki Shirai
    • 1
  • Koji Tsuruma
    • 1
  • Yuko Sakurai
    • 2
  • Satoshi Oyama
    • 3
  • Shin-ichi Minato
    • 1
    • 3
  1. 1.JST-ERATO MINATO Discrete Structure Manipulation System ProjectHokkaido UniversitySapporoJapan
  2. 2.Graduate School of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan
  3. 3.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

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