Mining Implicit Ratings for Focused Collaborative Filtering for Paper Recommendations

In this paper, we describe our on-going work on applying web mining to guide focused collaborative filtering for paper recommendations in a web-based learning system. In particular, we propose to first apply a data clustering technique on web usage data to form clusters (groups) of users with similar browsing patterns, which can be viewed as filtering based on implicit ratings (browsing sequences) according to [21]. Then, collaborative filtering techniques would be adopted on each cluster, instead of on the whole pool of users for recommendations as in other clustering-based collaborative filtering approaches. By using our two-layered collaborative filtering approach, we will not only maintain the diversity of users, but also focus on groups of users with similar browsing patterns. Therefore, our proposed approach could not only make personalized but also ‘grouplized’ recommendations, thus overcoming previous claims that data clustering will only produce ‘less-personal recommendations’ [33]. In addition, both explicit and implicit ratings are taken into consideration, which can reinforce and complement each other to make more accurate recommendations.