关键词: 评论文本, 图像信息, 兴趣点推荐, 矩阵分解, 神经网络

Abstract:

Due to the high sparsity of the user-POI（Point of Interest） check-in data, the traditional recommendation algorithm does not perform well in the recommendation of the point of interest. Therefore, a point of interest recommendation algorithm that combines text and image semantic information is proposed. In the recommendation process, the interpretability of user comments on ratings and the descriptiveness of image information on the appearance of points of interest are considered, and text and image assisted interest point recommendation is fully utilized. Firstly, the convolutional neural network is used to mine the comment text and image semantic information related to users and points of interest, and then construct the user-text semantic feature matrix, the interest point-image semantic feature matrix, and finally merge the user-interest rating matrix, based on the probability. Matrix decomposition constructs a unified recommendation model. Experiments show that the algorithm effectively alleviates the recommendation performance problem caused by the sparseness of the check-in data, and is superior to the mainstream method in terms of MAE（Mean Absolute Error） and RMSE（Root Mean Square Error）.

Key words: user reviews, image information, point of interest, matrix factorization, neural network