Incremental Set Recommendation Based on Class Differences

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

Abstract

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.

Keywords

recommendation classification collaborative filtering zero-suppressed binary decision diagram 

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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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