Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data

Social relationship plays an important role in life, and users are often affected by their friends' preferences. It is easier for users to choose items that their friends have purchased. In order to solve the cold start problem of the recommended system, a recommendation system that integrates social relationships is studied, and Bayesian personalized ranking review score social model and scalable Bayesian personalized ranking review score social model are proposed. The proposed fusion recommendation models integrate multi-source heterogeneous data such as scores, reviews, and social relationships from the data source level, introduce social relationships into the recommendation system through the user friend trust model, use the paragraph vector-distributed memory model to process review, use the fully connected neural network to process rating, and use an improved Bayesian personalized ranking model to optimize the ranking results. Experiments are conducted on the Yelp public dataset. It is shown that the recommendation accuracy of the two proposed models are better than other recommendation models. © 2021, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.