Behavioral Cost-Based Recommendation Model for Wanderers in Town
This paper proposes a new model for recommendation based on the behavioral cost of recommendees in town. The model is based on cost-benefit analysis of the information provided to the user, referring to the model of temporal discounting and preference reversal. Here we assume that behavioral cost may be regarded as time in temporal discounting. A recommender system based on this model can select information, which is located in the surrounding area (not so far away) and may be preferred by the user, if the system can estimate where the reversal phenomenon may occur. The experiments were made using an experimental social service, called “pin@clip”, which is an iPhone-based social bookmarking service in Shibuya, Tokyo, Japan that has been operating since December 2009. The experimental results show that the phenomenon of preference reversals might occur, even though the authors could not obtain statistically significant data.
Keywordscontext-aware computing location-based service recommender system behavioral cost user modeling
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