Using Restricted Random Walks for Library Recommendations

Abstract

Recommendations are a valuable help for library users e.g. striving to gain an overview of the important literature for a certain topic. We describe a new method for generating recommendations for documents based on clustering purchase histories. The algorithm presented here is called restricted random walk (RRW) clustering and has proven to cope efficiently with large data sets. Furthermore, as will be shown, the clusters are very well suited for giving recommendations in the context of library usage data. 2 Recommender systems and Cluster Algorithms for Library OPACs General classification schemes for recommender systems have been presented by Resnick and Varian [3], by Schafer et al. [1], and Gaul et al. [4].