A new collaborative recommender system addressing three problems

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. © Springer-Verlag Berlin Heidelberg 2004.