Title: Collaborative filtering recommendation based on conditional probability and weight adjusting

Authors: Haitao Wu; Wen-Kuang Chou; Ningbo Hao; Duan Wang; Jingfu Li

Addresses: Software School, Huanghuai University, 6 Kai-Yuan Road, Zhumadian, Henan 463000, China ' Software School, Huanghuai University, 6 Kai-Yuan Road, Zhumadian, Henan 463000, China ' Software School, Huanghuai University, 6 Kai-Yuan Road, Zhumadian, Henan 463000, China ' Software School, Huanghuai University, 6 Kai-Yuan Road, Zhumadian, Henan 463000, China ' Software School, Huanghuai University, 6 Kai-Yuan Road, Zhumadian, Henan 463000, China

Abstract: Collaborative filtering recommendation algorithm is one of the most successful technologies for building recommender systems. However, a user-based collaborative filtering method has its limits related to similarity and ratings. To avoid those limits, we propose a new item-based collaborative filtering algorithm based on conditional probability and weight adjusting in this paper. At first, any two items are selected to compute the similarity from common user ratings, and only the items with the similarity greater than preset thresholds are chosen as the set of supporting items. Then an integral parameter, that is the frequency of two items present simultaneously, is used to adjusted similarity weights. Finally, a new algorithm combining conditional probability and weight adjusting is proposed to predict ratings. The experimental results show the proposed algorithm is feasible and effective in practice.

Keywords: collaborative filtering recommendation; item-based filtering; conditional probability; weight adjusting; mean absolute error; MAE; recommender systems; similarity weights.

DOI: 10.1504/IJCSE.2015.067073

International Journal of Computational Science and Engineering, 2015 Vol.10 No.1/2, pp.164 - 170

Received: 10 Jan 2014
Accepted: 12 Jan 2014
Published online: 25 Jan 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article