Influence of Review Properties in the Usefulness Analysis of Consumer Reviews: A Review-Based Recommender System for Rating Prediction

Most e-commerce sites such as Amazon provide a comment function, and with the rapid growth of the number of comments, selecting and presenting useful comments helps users with decision-making. Recently, recommender systems using reviews instead of rating matrix enhance the recommendation quality by extracting the user preferences and item characteristics from the reviews. Some deep learning methods such as the attention mechanisms are used in these models to judge the review usefulness. However, these approaches rely on the historical data and do not perform well on the unseen reviews. In addition, the existing models ignore the sequential information embedded in the item reviews. In this work, we propose a deep learning model called review-based recommender with attentive properties (RRAP), which combines the review properties and sequential information to mitigate the problems in the traditional recommender systems. We perform experiments to compare the performance of the proposed recommender system with other recommender systems presented in the literature by using Amazon's four publicly available datasets. We use mean square error as an evaluation metric. The results show that the proposed RRAP reduces the prediction error and improves the interpretability of the model to a certain extent.