Generating Explanations for Explainable Recommendations Using Time-Series Information

Abstract

Abstract Generating explanations in natural language processing methods such as review generation and explainable recommendations plays an essential role in personalization. These methods can provide explanations that can help target users better understand the recommended items and enhance their awareness of the system’s knowledge about their preferences. However, existing explanation-generating methods for recommendations ignore time-series information, which is important to improve the effectiveness of the recommendation. To address this issue, we propose a Transformer-based explanation-generating method designed to generate recommendation reasons based on time-series information from sequential recommendations. This method bridges the time-series information from the target user’s purchase history and the item they may want to purchase to assign it a linguistic meaning and generate explanations for the recommended item. We divided our proposed method into two parts, sequential recommendation and reason generation, to maximize the benefits. Extensive experiments on three datasets show that the proposed approach can generate explanations for the results of sequential recommendations both reasonably and effectively compared to state-of-the-art explanation generation methods in most cases. Further experiments on time-series information and case studies verify the effectiveness of our approach. Our proposed model demonstrates its ability to generate explanations that incorporate time series information, as supported by our proposed evaluation metric. This also affirms the significant role of time series information in improving the performance and quality of the generated explanations.