Exploiting contextual information from event logs for personalized recommendation

Nowadays, recommender systems are widely used in various domains to help customers access to more satisfying products or services. It is expected that exploiting customers' contextual information can improve the quality of recommendation results. Most earlier researchers assume that they already have customers' explicit ratings on items and each rating has customer's abstracted context (e.g. summer, morning). However, in practical applications, it is not easy to obtain customers' explicit ratings and their abstract-level contexts. We aim to acquire customers' preferences and their context by exploiting the information implied in the customers' previous event logs and to adopt them into a well known recommendation technique, Collaborative Filtering (CF). In this paper, we show how to obtain customers' implicit preferences from event logs and present a strategy to abstract context information from event logs considering fuzziness in context. In addition, we present several methods to cooperate achieved contextual information and preferences into CF. To evaluate and compare our methods, we conducted several empirical experiments using a set of music listening logs obtained from last.fm, and the results indicate that our methods can improve the quality of recommendation.