Study on data sparsity in social network-based recommender system Online publication date: Wed, 23-Oct-2019
by Ru Jia; Ru Li; Meng Gao
International Journal of Computational Science and Engineering (IJCSE), Vol. 20, No. 1, 2019
Abstract: With the development of information technology and the expanding of information resources, it is more difficult for people to get the information that they are really interested in, which is so-called information overload. Recommender systems are regarded as an important approach to deal with information overload, because it can predict users' preferences according to users' records. Matrix factorisation is very successful in recommender systems, but it faces the terrible problem of data sparsity. In this paper, the authors deal with the sparsity problem from the perspective of adding more kinds of information from social networks, such as friendships and tags into the recommending model in order to alleviate the sparsity problem. The paper also validates the impacts of users' friendships, tags and neighbours of items on reducing the sparseness of the data and improving the accuracy of recommending by the experiments using the dataset from real life.
Online publication date: Wed, 23-Oct-2019
Go to Inderscience Online Journals to access the Full Text of this article.
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:
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 firstname.lastname@example.org