Context-aware users' preference models by integrating real and supposed situation data

Chihiro Ono, Yasuhiro Takishima, Yoichi Motomura, Hideki Asoh, Yasuhide Shinagawa, Michita Imai, Yuichiro Anzai

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


This paper proposes a novel approach of constructing statistical preference models for context-aware personalized applications such as recommender systems. In constructing context-aware statistical preference models, one of the most important but difficult problems is acquiring a large amount of training data in various contexts/situations. In particular, some situations require a heavy workload to set them up or to collect subjects capable of answering the inquiries under those situations. Because of this difficulty, it is usually done to simply collect a small amount of data in a real situation, or to collect a large amount of data in a supposed situation, i.e., a situation that the subject pretends that he is in the specific situation to answer inquiries. However, both approaches have problems. As for the former approach, the performance of the constructed preference model is likely to be poor because the amount of data is small. For the latter approach, the data acquired in the supposed situation may differ from that acquired in the real situation. Nevertheless, the difference has not been taken seriously in existing researches. In this paper we propose methods of obtaining a better preference model by integrating a small amount of real situation data with a large amount of supposed situation data. The methods are evaluated using data regarding food preferences. The experimental results show that the precision of the preference model can be improved significantly.

Original languageEnglish
Pages (from-to)2552-2559
Number of pages8
JournalIEICE Transactions on Information and Systems
Issue number11
Publication statusPublished - 2008 Nov


  • Context-awareness
  • Probabilistic modeling
  • Recommender systems
  • User preference model

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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