ConciseExplain: Reducing Redundancy and Spuriousness in Persuasive Recommendation Explanation

Recommendation systems are effective tools for information filtering and discovery. These systems are widely applied across various consumer sectors and hold significant potential for applications in professional domains to enhance work efficiency. However, supporting decision-making in professional contexts requires not only providing recommendation results but also offering explanations to persuade users to adopt the suggestions. Taking the task in the primary bond market as an example, where sales staff seek potential investors for bonds, this paper presents the development and deployment of a recommendation system designed for a professional setting. The system provides a set of key features as explanations for its recommendations. In this process, we observe that current explanation methods may select redundant and spurious features, which can undermine the persuasive impact of the explanations. To address this issue, we propose a method named ConciseExplain, which leverages a mask training strategy and gradient descent to directly identify a concise set of features. We conduct experiments on real-world and synthetic datasets. Our method achieves relative improvements of 6.1% and 12.4% over the best-performing baseline on redundant and spurious metrics, respectively. Our method also outperforms the baseline method in online manual evaluations. Moreover, during the one-year official deployment of our system at China Securities Co., Ltd. (a leading brokerage firm in China), we observed a continuous improvement in the accuracy of the recommendation system. This suggests that, with concise explanations, a positive feedback loop might be established between recommendation outcomes and investment decisions.