Interest-Based User Grouping Model for Collaborative Filtering in Digital Libraries

Research in recommender systems focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users. – where it is possible to classify items involved and to make recommendations based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as Digital Libraries, additional capabilities are needed for recommender systems to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. In this paper, we present an interest-based user grouping model for a collaborative recommender system for Digital Libraries. Also, we present several user interfaces that obtain implicit user rating data. Our model uses a high performance document clustering algorithm, LINGO, to extract document topics and user interests from documents users access in a Digital Library. This model is better suited to Digital Libraries than traditional recommender systems because it focuses more on users than items and because it utilizes implicit rating data.