Recommendation Based on Review Texts and Social Communities: A Hybrid Model

Abstract

With the development of e-commerce, a large amount of personalized information is produced daily. To utilize diverse personalized information to improve recommendation accuracy, we propose a hybrid recommendation model based on users' ratings, reviews, and social data. Our model consists of six steps, review transformation, feature generation, community detection, model training, feature blending, and prediction and evaluation. Three groups of experiments are performed in this paper. Experiments A are used to identify the regression algorithm used in our model, Experiments B are used to identify the model to analyze review texts and the algorithm to detect social communities, and Experiments C compare our hybrid recommendation model with conventional recommendation models, such as probabilistic matrix factorization, UserKNN, ItemKNN, and social network-based models, such as socialMF and TrustSVD. The experiment results show that recommendation accuracy can be improved significantly with our hybrid model based on review texts and social communities.