Abstract
In this contribution we describe a new way of building a recommender service based on OPAC web-usage histories. The service is based on a clustering approach with restricted random walks. This algorithm has some properties of single linkage clustering and suffers from the same deficiency, namely bridging. By introducing the idea of a walk context (see Franke and Thede (2005) and Franke and Geyer-Schulz (2004)) the bridging effect can be considerably reduced and small clusters suitable as recommendations are produced. The resulting clustering algorithm scales well for the large data sets in library networks. It complements behavior-based recommender services by supporting the exploration of the revealed semantic net of a library network’s documents and it offers the user the choice of the trade-off between precision and recall. The architecture of the behavior-based system is described in Geyer-Schulz et al. (2003).
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Franke, M., Geyer-Schulz, A., Neumann, A. (2006). Building Recommendations from Random Walks on Library OPAC Usage Data. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_27
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