A Review Semantics Based Model for Rating Prediction

A review expresses the concerned aspects and corresponding assessments a customer has towards a particular item. Extracting the user's interests and product's features from their aggregated reviews and matching them together to predict the overall rating is a common paradigm in a review-based recommender. However, such a paradigm trains model on the aggregated historical review which grows with time and may have much conflicting semantics, thus the scalability and accuracy may be compromised. In this paper, a novel review semantics based model(RSBM) is proposed to enhance the performance of the review-based recommender. It consists of three parts: the review semantics extractor, the review semantics generator and the rating regressor. Firstly, the review semantics extractor uses a convolutional neural network(CNN) to extract the semantic features of a particular review text. Secondly, the semantics generator uses a memory-network liked structure and attention mechanism to simulate the decision-making process which assesses each concerned aspect of an item to generate the review semantics. In the training phase, the generated semantics is compared with the semantics extracted by the review semantics extractor. In the last, the generated semantic features are fed into the rating regressor to predict the overall rating. Experiments on a series of reality datasets show that the proposed model gains better performances than several state-of-the-art recommendation approaches in terms of accuracy and scalability.