test
server time: root: http://itiis.org
current_path: /journals/tiis/digital-library/manuscript/2008
current_url: http://itiis.org/journals/tiis/digital-library/manuscript/2008
Study on Tag, Trust and Probability Matrix Factorization Based Social Network Recommendation
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Study on Tag, Trust and Probability Matrix Factorization Based Social Network Recommendation

Vol. 12, No. 5, May 30, 2018
10.3837/tiis.2018.05.010 , Download Paper (Free):

Abstract

In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users’ preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available Last.fm dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
Z. Liu and H. Zhong, "Study on Tag, Trust and Probability Matrix Factorization Based Social Network Recommendation," KSII Transactions on Internet and Information Systems, vol. 12, no. 5, pp. 2082-2102, 2018. DOI: 10.3837/tiis.2018.05.010 .

[ACM Style]
Zhigang Liu and Haidong Zhong. 2018. Study on Tag, Trust and Probability Matrix Factorization Based Social Network Recommendation. KSII Transactions on Internet and Information Systems, 12, 5, (2018), 2082-2102. DOI: 10.3837/tiis.2018.05.010 .