Abstract
With the development of e-commerce and information access, a large amount of information can be found online, which makes a good recommendation service to be urgently necessary. While many collaborative recommender systems (CRS) have succeeded in capturing the similarity among users or items based on ratings, there are still some challenges for them to be a more efficient RS. In this paper, we address three problems in CRS, that is user bias, non-transitive association, and new item problem, and show that the ICHM suggested in our previous work is able to solve the addressed problems. A series of experiments are carried out to show that our approach is feasible.
This work was supported by grant No. R05-2004-000-10190-0 from the Korea Science and Engineering Foundation.
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Kim, B.M., Li, Q., Kim, JW., Kim, J. (2004). A New Collaborative Recommender System Addressing Three Problems. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_53
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DOI: https://doi.org/10.1007/978-3-540-28633-2_53
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