Using Restricted Random Walks for Library Recommendations

Markus Franke, Andreas Geyer-Schulz



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.


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Paper Citation

in Harvard Style

Franke M. and Geyer-Schulz A. (2005). Using Restricted Random Walks for Library Recommendations . In Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005) ISBN 972-8865-38-4, pages 107-115. DOI: 10.5220/0001412001070115

in Bibtex Style

author={Markus Franke and Andreas Geyer-Schulz},
title={Using Restricted Random Walks for Library Recommendations},
booktitle={Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)},

in EndNote Style

JO - Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)
TI - Using Restricted Random Walks for Library Recommendations
SN - 972-8865-38-4
AU - Franke M.
AU - Geyer-Schulz A.
PY - 2005
SP - 107
EP - 115
DO - 10.5220/0001412001070115