Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023)

Abstract: Explainable recommendation is a technique that combines prediction and generation tasks to produce more persuasive results. Among these tasks, textual generation demands large amounts of data to achieve satisfactory accuracy. However, historical user reviews of items are often insufficient, making it challenging to ensure the precision of generated explanation text. To address this issue, we propose a novel model, ERRA (Explainable Recommendation by personalized Review retrieval and Aspect learning). With retrieval enhancement, ERRA can obtain additional information from the training sets. With this additional information, we can generate more accurate and informative explanations. Furthermore, to better capture users’ preferences, we incorporate an aspect enhancement component into our model. By selecting the top-n aspects that users are most concerned about for different items, we can model user representation with more relevant details, making the explanation more persuasive. To verify the effectiveness of our model, extensive experiments on three datasets show that our model outperforms state-of-the-art baselines (for example, 3.4% improvement in prediction and 15.8% improvement in explanation for TripAdvisor).

## introduction recent years have witnessed a growing interest in the development of explainable recommendation models @xcite . in general, there are three different kinds of frameworks for explainable recommendation models, which are post-hoc @xcite , embedded @xcite and multi-task learning methods @xcite . post-hoc methods generate explanations for a pre-trained model after the fact, leading to limited diversity in explanations. ## problem statement our task is to develop a model that can accurately predict ratings for a specific product and provide a reasonable explanation for the corresponding prediction. the model's input is composed of various elements, namely the user id, item id, aspects, reviews, and retrieval sentences, whereas the resulting output of the model encompasses both a prediction and its explanation. we offer a detailed description of our models' input and output data in this section. ## conclusion in this paper, we propose a novel model, called erra, that integrates personalized aspect selection and retrieval enhancement for prediction and explanation tasks. to address the issue of incorrect embedding induced by data sparsity, we incorporate personalized aspect information and rich review knowledge corpus into our model. experimental results demonstrate that our approach is highly effective compared with state-of-the-art baselines on both the accuracy of recommendations and the quality of corresponding explanations.