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  • Open Access

    ARTICLE

    Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network

    Sheng Bin1,*, Gengxin Sun1, Ning Cao2, Jinming Qiu2, Zhiyong Zheng3, Guohua Yang4, Hongyan Zhao5, Meng Jiang6, Lina Xu7

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 659-674, 2019, DOI:10.32604/cmc.2019.05858

    Abstract Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multi-relational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. Then the user-item rating matrix… More >

  • Open Access

    ARTICLE

    Seed Selection for Data Offloading Based on Social and Interest Graphs

    Ying Li1, Jianbo Li1,*, Jianwei Chen1, Minchao Lu1, Caoyuan Li2,3

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 571-587, 2018, DOI:10.32604/cmc.2018.02851

    Abstract The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user pairs. So, a contact graph… More >

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