| Abstract: |
To engage visitors to a Web site at a very early stage (i.e.,
before registration or authentication), personalization tools
must rely primarily on clickstream data captured in Web server
logs. The lack of explicit user ratings as well as the sparse
nature and the large volume of data in such a setting poses
serious challenges to standard collaborative filtering
techniques in terms of scalability and performance. Web usage
mining techniques such as clustering that rely on offline
pattern discovery from user transactions can be used to improve
the scalability of collaborative filtering, however, this is
often at the cost of reduced recommendation accuracy. In this
paper we propose effective and scalable techniques for Web
personalization based on association rule discovery from usage
data. Through detailed experimental evaluation on real usage
data, we show that the proposed methodology can achieve better
recommendation effectiveness, while maintaining a computational
advantage over direct approaches to collaborative filtering
such as the $k$-nearest-neighbor strategy.
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