Abstract
We propose a simple recommendation system based on rough set theory. When the user searches some products by a product retrieval system, if the user does not have enough information of the products, it is very difficult to represent relevant queries. Thus, by data mining and Kansei information processing, product recommendation system estimates user's preference from user's queries, and provides information about products that the user may prefer. Our recommendation method constructs decision rules from user's query, and recommends some products by estimating implicit conditions of products based on decision rules. Recommended products do not agree with the query, however, recommended products satisfy estimated implicit conditions, and therefore the user may prefer recommended products. We also propose a criterion to evaluate recommended products based on certainty and coverage of decision rules. Moreover, we evaluate our recommendation method and evaluation method by experiment, and discuss issues for improvement of our methods based on results of the experiment.
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