Improved user-based collaborative filtering algorithm with topic model and time tag
by Liu Na; Lu Ying; Tang Xiao-Jun; Li Ming-Xia; Chunli Wang
International Journal of Computational Science and Engineering (IJCSE), Vol. 22, No. 2/3, 2020

Abstract: Collaborative filtering algorithms make use of interaction rates between users and items for generating recommendations. Similarity among users is calculated based on rating mostly, without considering explicit properties of users involved. Considering the number of tags of a user can direct response the user preference to some extent, we propose a collaborative filtering algorithm using topic model called user-item-tag latent Dirichlet allocation (UITLDA) in this paper. UITLDA model consists of two parts. The first part is active user with its item. The second part is active user with its tag. We form topic model from these two parts respectively. The two topics constrain each other and integrate into a new topic distribution. This model not only increases the user's similarity, but also reduces the density of the matrix. In prediction computation, we also introduce time delay function to increase the precision. The experiments showed that the proposed algorithm achieved better performance compared with baseline on MovieLens datasets.

Online publication date: Mon, 18-May-2020

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