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A Personalized Location Recommendation based on Convolutional Neural Network

Location recommendation is an important research content of recommendation system, but it often faces the problems of sparse data and low degree of personalization. The top-k recommendation is selected as the research objective to model users' rating behavior of explicit feedback behavior. A personalized location recommendation algorithm LRA-CNN based on convolutional neural network (CNN) is designed and implemented. The LRA-CNN combines various features between locations and study their joint influence between users. More concretely, co-appearing and geography effects in locations are used to alleviate check-in data sparse matter in location recommendation, and converted into the feature vector representation of users and locations by feature embedding. Besides, the embedding users and locations are fed into CNN for learning high-order interactions among various features adaptively. Experimental results show that compared with several traditional methods, the proposed algorithm can effectively improve the accuracy of location recommendation.