POI Recommendation Method Based on Multi-Source Information Fusion Using Deep Learning in Location-Based Social Networks
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초록
Sign-in point of interest (POI) are extremely sparse in location-based social networks, hinderingrecommendation systems from capturing users’ deep-level preferences. To solve this problem, we propose acontent-aware POI recommendation algorithm based on a convolutional neural network. First, usingconvolutional neural networks to process comment text information, we model location POI and user latentfactors. Subsequently, the objective function is constructed by fusing users’ geographical information andobtaining the emotional category information. In addition, the objective function comprises matrixdecomposition and maximisation of the probability objective function. Finally, we solve the objective functionefficiently. 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. Comparativeexperiments show that the proposed method can obtain a higher precision rate than several other newerrecommended methods.
Abstract
Sign-in point of interest (POI) are extremely sparse in location-based social networks, hinderingrecommendation systems from capturing users’ deep-level preferences. To solve this problem, we propose acontent-aware POI recommendation algorithm based on a convolutional neural network. First, usingconvolutional neural networks to process comment text information, we model location POI and user latentfactors. Subsequently, the objective function is constructed by fusing users’ geographical information andobtaining the emotional category information. In addition, the objective function comprises matrixdecomposition and maximisation of the probability objective function. Finally, we solve the objective functionefficiently. 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. Comparativeexperiments show that the proposed method can obtain a higher precision rate than several other newerrecommended methods.
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