Improving explainable recommendations by deep review-based explanations

Many e-commerce sites encourage their users to write product reviews, in the knowledge that they exert a considerable influence on users’ decision-making processes. These snippets of real-world experience provide an essential source of data for interpretable recommendations. However, current methods relying on user-generated content to make recommendations can run into problems because of well-known issues with reviews, such as noise, sparsity and irrelevant content. On the other hand, recent advances in text generation methods demonstrate significant text quality improvements and show promise in their ability to address these problems. In this paper, we develop two character-level deep neural network-based personalised review generation models, and improve recommendation accuracy by generating high-quality text which meets the input criteria of text-aware recommender systems. To make fair comparisons, we train review-aware recommender systems by human written reviews and attain advanced recommendations by feeding generated reviews at the inference step. Our experiments are conducted on four large review datasets from multiple domains. We leverage our methods’ performance by comparing with non-review based recommender systems and advanced review-aware recommender systems. The results demonstrate that we beat baselines on a range of metrics and obtain state-of-the-art performance on both rating prediction and top- N ranking. Our sparsity experiments validate that our generation models can produce high-quality text to tackle the sparsity problem. We also demonstrate the generation of useful reviews so that we can achieve up to 13.53% RMSE improvements. For explanation evaluation, quantitative analyses reveal good understandable scores for our generated review-based explanations, and qualitative case studies substantiate we can capture critical aspects in generating explanations.