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http://dx.doi.org/10.3745/JIPS.01.0068

POI Recommendation Method Based on Multi-Source Information Fusion Using Deep Learning in Location-Based Social Networks  

Sun, Liqiang (Dept. of Information Technology, Qingdao Vocational and Technical College of Hotel Management)
Publication Information
Journal of Information Processing Systems / v.17, no.2, 2021 , pp. 352-368 More about this Journal
Abstract
Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.
Keywords
Convolutional Neural Network; Emotional Information; Geographical Information; Latent Factor Modelling; Location-Based Social Network; Objective Function;
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