USING RESTRICTED RANDOM WALKS FOR LIBRARY RECOMMENDATIONS AND KNOWLEDGE SPACE EXPLORATION

Abstract

Implicit recommender systems provide a valuable aid to customers browsing through library corpora. We present a method to realize such a recommender especially for, but not limited to, libraries. The method is cluster-based, scales well for large collections, and produces recommendations of good quality. The approach is based on using session histories of visitors of the library's online catalog in order to generate a hierarchy of nondisjunctive clusters. Depending on the user's needs, the clusters at different levels of the hierarchy can be employed as recommendations. Using the prototype of a user interface we show that, if, for instance, the user is willing to sacrifice some precision in order to gain a higher number of documents during a specific session, he or she can do so easily by adjusting the cluster level via a slider.