Abstract：With the fast development of micro-blogs, there is an unprecedented abundance of information shared online. Meanwhile, information overload appears. Due to the continually increasing nonstructural contents and the complexity that characterizes the structure of social networks, the deployment of online customized recommendation systems is very challenging. In this article a collaborative filtering recommendation model integrating information diffusion has been designed to solve the problem of data sparsity and cold-start problem in the context of micro-blog websites. Firstly, the proposed framework constructs a user-keyword interest model by using natural language processing technology to handle nonstructural contents.extracted thereafter act as recommended items. Secondly, the research uses a first-order Markov random work model to simulate the processing of users' preference' diffusion in social networks. The user-keyword preference matrix is then conducted. Lastly, the experiments are conducted with real world dataset from Weibo. The effectiveness of the proposed recommendation model is evaluated by using three assessment criteria (mean absolute error, precision and recall). The results show that the collaborative filtering recommendation model integrating information diffusion performs better than benchmark models which do not consider social networks.
蔡淑琴, 袁乾, 周鹏, 梁烽. 基于信息传播理论的微博协同过滤推荐模型[J]. 系统工程理论与实践, 2015, 35(5): 1267-1275.
CAI Shu-qin, YUAN Qian, ZHOU Peng, LIANG Feng. Collaborative filtering recommendation model in micro-blogging website based on information diffusion theory. Systems Engineering - Theory & Practice, 2015, 35(5): 1267-1275.
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2015, Vol. 35 
