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 information and item information obtained from data explicitly entered by users. In these systems, it is possible to classify the 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, very sparse rating data, very biased rating data on specific items, and serious challenges in getting explicit rating data from users. Further, the items in Digital Libraries are hard to categorize, especially since many research interest topics are quite narrow. In this paper, we present an interest-based user grouping model for a collaborative recommender system for Digital Libraries. Our model uses a high performance document clustering algorithm, LINGO, to extract document topics and user interests from the documents users access in a Digital Library. Also, we present several user interfaces that obtain implicit user rating data. An experiment was carried out to verify our hypotheses. This model is better suited to Digital Libraries than traditional recommender systems because it focuses more on users rather than items and because it utilizes implicit rating data. Moreover, the document clustering algorithm mitigates data sparseness problems.