Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
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Sensibility-vectors kansei-vectors are useful for retrieving objects like pictures, music, perfumes, and apparels on the Internet. The sensibility-vector is an array of values, each indicating a degree of feeling or impression represented as sensibility word or kansei word. However, even such an approach leaves a gap between user's subjective sensibility image, feeling value and the corresponding one stored in the database. This paper proposes a search method to automatically and adaptively decrease such gaps by estimating a subjective criterion deviation SCD of the user's search histories and fuzzy modeling. Conventional methods need tests and questionnaires beforehand to infer user's individual sensibility to his or her instinct or impression. The proposed method automatically decreases such gaps without users' burden caused by such conventional methods as requiring questionnaires. Moreover, this method reflects the dynamic changes in user's preferences. Namely, this method does not need to know user's preferences beforehand with questionnaires. An experiment was conducted by building and using a perfume search system. Experimental data results showed that the proposed method is effective.