Collaborative filtering recommendation based on conditional probability and weight adjusting
by Haitao Wu; Wen-Kuang Chou; Ningbo Hao; Duan Wang; Jingfu Li
International Journal of Computational Science and Engineering (IJCSE), Vol. 10, No. 1/2, 2015

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.

Online publication date: Sun, 25-Jan-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com