Gongshen Liu1, Kui Meng1,*, Jiachen Ding1, Jan P. Nees1, Hongyi Guo1, Xuewen Zhang1
CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 101-120, 2019, DOI:10.32604/cmc.2019.03898
Abstract Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user’s social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users’ interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first… More >
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