Predicting Preference Tags to Improve Item Recommendation

Collaborative filtering (CF) based recommender systems identify and recommend interesting items to a given user based on the user’s past rating activity. These systems improve their recommendations by identifying user preferences and item related information from external sources, like reviews written by users, or concept tags shared by users about these items. These preferences are often reflected through a multi-criterion rating. In this study, we seek to improve recommender systems by integrating user preferences as side information within standard neighborhoodbased and matrix factorization based methods. We assume that a user’s choice of tags for an item provides additional information about the user’s personal preference and additional features about the item. Since, querying users to provide tags and multi-criteria rating imposes an additional burden on the user base, we propose using collective classification to predict tags for both the users and items. We also investigate the use of active learning approaches integrated within the collective classification framework when tag information (users or items) is limited. Our experimental results on several real world datasets show the advantages of using tag-based information within the recommender systems. We are also able to show the effectiveness of collective classification algorithms in estimating user preferences and item features. Keywords— Tag-based Recommendation System, Active Learning, Collective Classification