%0 Journal Article
%A JI Zhen-yan
%A PI Huai-yu
%A YAO Wei-na
%T A Hybrid Recommendation Model Based on Fusion of Multi-Source Heterogeneous Data
%D 2019
%R 10.13190/j.jbupt.2018-176
%J Journal of Beijing University of Posts and Telecommunications
%P 126-132
%V 42
%N 1
%X In order to reflect users' personalized preferences more comprehensively and improve the accuracy of recommendation, a hybrid recommendation model based on fusion of multi-source heterogeneous data is proposed. This model takes the impacts from both users' social relationships and reviews on ratings into account. Topics are extracted from reviews as user features and business features, and then communities are divided for users via a community discovery algorithm. Finally, a machine learning algorithm is used to model user communities in order to predict ratings. Businesses are ranked based on predicted ratings and then the top <em>N</em> businesses are recommended to the user. The experimental results show that the proposed hybrid recommendation model can improve the rating prediction accuracy and recommendation accuracy compared with the conventional recommendation algorithms.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-176