Abstract
A system applicable in electronic commerce environments that combines the strengths of both collaborative filtering and data mining for providing better recommendations is presented. It captures the item-to-item relationship through association rule mining and then uses purchase behaviour of collaborative users for generating the recommendations. It was implemented and evaluated on a set of real datasets. Our methodology results in improved quality of recommendations measured in terms of recall and coverage metrics.
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© 2006 Springer-Verlag Berlin Heidelberg
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Bhasker, B., Park, HH., Park, J., Kim, HS. (2006). Product Recommendations for Cross-Selling in Electronic Business. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_119
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DOI: https://doi.org/10.1007/11941439_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
eBook Packages: Computer ScienceComputer Science (R0)