Recommendation algorithm combining the user-based classified regression and the item-based filtering

Authors:
Yu Chuan;Xu Jieping;Du Xiaoyong
Affiliations:
Renmin University, Beijing, China;Renmin University, Beijing, China;Renmin University, Beijing, China
Venue:
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Year:
2006

Citing 4
Cited 1

GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Learning and Revising User Profiles: The Identification ofInteresting Web Sites

Machine Learning - Special issue on multistrategy learning
Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web

Personalized rough-set-based recommendation by integrating multiple contents and collaborative information

Information Sciences: an International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

With the expansion of the Internet services, providing personalized product recommendations has become one of the most important ways to attract customers. Especially, collaborative recommender systems have achieved widespread success on the web. Information of products is recommended to the users based on their nearest "neighbors" who have similar interests. It is widely known that there is a sparsity problem in such systems. However, according to our research, there are other problems: one is that the typical collaborative algorithm loses some important parameter when it predicts the ratings, because there might be a strong similarity between the users who give very different ratings. Another is that the classification information of resources is not used. To solve these problems, we have proposed a recommendation algorithm combining the user-based classified regression and the item-based filtering. The experiment results show that performance is improved after applying the new algorithm.