Artificial Intelligence Lab, Department of MIS, University of Arizona / Information overload on the Web has created enormous
challenges to customers selecting products for online
purchases and to online businesses attempting to identify
customersâ preferences efficiently. Various recommender
systems employing different data representations
and recommendation methods are currently used
to address these challenges. In this research, we developed
a graph model that provides a generic data representation
and can support different recommendation
methods. To demonstrate its usefulness and flexibility,
we developed three recommendation methods: direct
retrieval, association mining, and high-degree association
retrieval. We used a data set from an online bookstore
as our research test-bed. Evaluation results
showed that combining product content information and
historical customer transaction information achieved
more accurate predictions and relevant recommendations
than using only collaborative information. However,
comparisons among different methods showed
that high-degree association retrieval did not perform
significantly better than the association mining method
or the direct retrieval method in our test-bed.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105683 |
| Date | January 2004 |
| Creators | Huang, Zan, Chung, Wingyan, Chen, Hsinchun |
| Publisher | Wiley Periodicals, Inc |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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