Artificial Intelligence Lab, Department of MIS, University of Arizona / Research shows that recommendations comprise a valuable
service for users of a digital library [11]. While most existing
recommender systems rely either on a content-based
approach or a collaborative approach to make
recommendations, there is potential to improve
recommendation quality by using a combination of both
approaches (a hybrid approach). In this paper, we report how
we tested the idea of using a graph-based recommender
system that naturally combines the content-based and
collaborative approaches. Due to the similarity between our
problem and a concept retrieval task, a Hopfield net
algorithm was used to exploit high-degree book-book, useruser
and book-user associations. Sample hold-out testing and
preliminary subject testing were conducted to evaluate the
system, by which it was found that the system gained
improvement with respect to both precision and recall by
combining content-based and collaborative approaches.
However, no significant improvement was observed by
exploiting high-degree associations.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105313 |
| Date | January 2002 |
| Creators | Huang, Zan, Chung, Wingyan, Ong, Thian-Huat, Chen, Hsinchun |
| Publisher | ACM/IEEE-CS |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
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