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  • × author_ss:"Li, D."
  • × theme_ss:"Internet"
  1. Shen, X.; Li, D.; Shen, C.: Evaluating China's university library Web sites using correspondence analysis (2006) 0.03 
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    Abstract
    In recent years, many evaluations of Web sites have been conducted, and relevant researches have also been carried out in academic circles. Correspondence analysis is introduced in this paper to evaluate university library Web sites through building a correspondence analysis model. This paper gives suggestions as to how to construct university library Web sites based on analysis and summary of evaluation results, in a bid to strengthen the construction of university library Web sites.
    Date
    22. 7.2006 16:40:18
    Type
    a
  2. Li, D.; Luo, Z.; Ding, Y.; Tang, J.; Sun, G.G.-Z.; Dai, X.; Du, J.; Zhang, J.; Kong, S.: User-level microblogging recommendation incorporating social influence (2017) 0.00 
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    Abstract
    With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.
    Type
    a

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