Context-Aware Preference Model Based on a Study of Difference between Real and Supposed Situation Data

Authors:
Chihiro Ono;Yasuhiro Takishima;Yoichi Motomura;Hideki Asoh
Affiliations:
KDDI R&D Laboratories, Saitama, Japan 356-8502;KDDI R&D Laboratories, Saitama, Japan 356-8502;Digital Human RC, AIST, Tokyo, Japan 135-0064;Intelligent Systems RI, AIST, Ibaraki, Japan 305-8568
Venue:
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Year:
2009

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Abstract

We propose a novel approach for constructing statistical preference models for context-aware recommender systems. To do so, one of the most important but difficult problems is acquiring sufficient training data in various contexts/situations. Particularly, some situations require a heavy workload to set them up or to collect subjects under those situations. To avoid this, often a large amount of data in a supposed situation is collected, i.e., a situation where the subject pretends/imagines that he/she is in a specific situation. Although there may be difference between the preference in the real situation and the supposed situation, this has not been considered in existing researches. Here, to study the difference, we collected a certain amount of corresponding data. We asked subjects the same question about preference both in the real and the supposed situation. Then we proposed a new model construction method using a difference model constructed from the correspondence data and showed the effectiveness through the experiments.