Research on location and similar comments in point-of-interest recommendation system for users
Yu Liu, Ning Wei · Journal of Physics Conference Series · 2021
Abstract By combining deep learning algorithms and potential probabilistic topic models, this paper proposes a new modeling strategy for individual users and constructs an effective point of interest recommendation system. First, Doc2vec is used to vectorize the user’s comments, and the Bow model is used to match the similarity of the points of interest, and then the potential probability model is used to cluster the positions of the points of interest. Finally, relying on the user’s historical comment data as a basis, the Top-K ranking recommendation scheme is obtained. The experimental results show that for large-scale location-based social network data sets, the algorithm in this paper has an ideal final effect on users’ personalized recommendations. The recommendation model for user location information and similar comment points of interest proposed in this paper is more subjectively and objectively in line with user behavior needs. On the premise of ensuring the real-time performance of recommendation, the model proposed in this paper effectively improves the accuracy of recommendation compared with traditional point-of-interest recommendation algorithms.