A Reinforcement Learning Framework for Explainable Recommendation

By Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, Xing Xie. 2018

163 citations recorded by the source corpus.

Explainable recommendation, which provides explanations about why an item is recommended, has attracted increasing attention due to its ability in helping users make better decisions and increasing users' trust in the system. Existing explainable recommendation methods either ignore the working mechanism of the recommendation model or are designed for a specific recommendation model. Moreover, it is difficult for existing methods to ensure the presentation quality of the explanations (e.g., consistency). To solve these problems, we design a reinforcement learning framework for explainable recommendation. Our framework can explain any recommendation model (model-agnostic) and can flexibly control the explanation quality based on the application scenario. To demonstrate the effectiveness of our framework, we show how it can be used for generating sentence-level explanations. Specifically, we instantiate the explanation generator in the framework with a personalized-attention-based neural network. Offline experiments demonstrate that our method can well explain both collaborative filtering methods and deep-learning-based models. Evaluation with human subjects shows that the explanations generated by our method are significantly more useful than the explanations generated by the baselines.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Xu Chen
4. Yongfeng Zhang
5. Tat-seng Chua
6. Meng Wang
7. Fuli Feng
8. Xing Xie

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Xu Chen
4. Yongfeng Zhang
5. Meng Wang
6. Fuli Feng
7. Tat-seng Chua
8. Xing Xie

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “The coupled agents learn to select the explanations that best approximate the predictions made by the recommendation model (Wang et al., 2018d).”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Reinforcement learning [13], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [156], [157], [158], [159], [160] Others [161], [162]”
• Explainable Recommendation through Attentive Multi-View Learning (2019, methodology): “Explainability of the model, which is the ability to provide explanations for why an item is recommended, is considered equally important (Wang et al. 2018a).”
• Co-Attentive Multi-Task Learning for Explainable Recommendation (2019, methodology): “, 2018] and RLRec [Wang et al., 2018b], which obtain the important sentences or reviews by employing the attention mechanism.”
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation (2019, background): “There have been attempts to invoke RL in recommender systems in a non-KG setting, such as for ads recommendation [25], news recommendation [35] and post-hoc explainable recommendation [27].”
• Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “One interesting application of explainable recommendation is in debugging the failed RS [152].”
• Towards Knowledge-Based Recommender Dialog System (2019, unknown): “Besides accuracy, explainability is also an important aspect when evaluating recommender systems (Zhang et al., 2014; Zhang and Chen, 2018; Wang et al., 2018b).”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, unknown): “such as review-level explanations [109], [110].”
• When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “Thus, they are at the forefront of research [106], [118], [119], [120], [121].”