Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm
by Mojtaba Salehi; Isa Nakhai Kamalabadi; Mohammad Bagher Ghaznavi-Ghoushchi
International Journal of Business Information Systems (IJBIS), Vol. 13, No. 3, 2013

Abstract: Recommender system technology can assist customers of a company to choose an appropriate product or service after learning their preferences. But this technology suffers from some problems such as scalability and sparsity. Since users express their opinions implicitly based on some specific attributes of items, this paper proposes a collaborative filtering algorithm based on attributes of items to address these problems. Attributes weight vector for each user is considered as a chromosome in genetic algorithm. This algorithm optimises the weights according to historical rating. A weighted C-means algorithm also is introduced to cluster users based on the optimised attributes weight vector. Finally, recommendation is generated by a user based similarity in each cluster. The experimental results show that our proposed method outperforms current algorithms and can perform superiorly and alleviates problems such as sparsity and precision quality. The main contribution of this paper is addressing sparsity problem using attribute weighting and scalability problem using weighted C-means algorithm.

Online publication date: Fri, 27-Sep-2013

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 Business Information Systems (IJBIS):
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