期刊
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
卷 55, 期 -, 页码 534-543出版社
ELSEVIER
DOI: 10.1016/j.future.2014.05.002
关键词
Microblogging; Followee recommendation; Multiple source information
资金
- NSFC fund [61370233]
- Foundation for the Author of National Excellent Doctoral Dissertation of PR China [201345]
- Ministry of Education and China Mobile Communications Corporation (MoE-CMCC) Research Founding [MCM20130382]
- Research Fund for the Doctoral Program of Higher Education of China [20110142120080]
- Fundamental Research Funds for the Central Universities [2014YQ014]
Followee recommendation plays an important role in information sharing over microblogging platforms. We frame this problem as a top-k ranking in collaborative filtering (CF). The difficulty is that explicit user to-user ratings are not available on microblogging systems. Thus existing CF schemes are not applicable to followee recommendation over microblogging systems. To solve this problem, in this paper, we propose a novel followee ranking scheme using a variation of the latent factor model, which leverages implicit users' feedback including both tweet content and social relation information. To achieve good top-k recommendation, we introduce a rank-based criterion to latent factor model (LFM). The main obstacle for training the model parameters is the non-smoothness of the objective function of LFM, which makes traditional parameter optimization methods infeasible. To tackle with the problem, we further design a smooth version of the objective function. We conduct comprehensive experiments on a large-scale dataset collected from Sina Weibo, the most popular microblogging system in China and a real world experiment on the Amazon Mechanical Turk CrowdSourcing platform to evaluate the performance of our design. The results show that our scheme greatly outperforms existing schemes in terms of precision and top-k ranking by 46.8% and 32.8%, respectively. (C) 2014 Elsevier B.V. All rights reserved.
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