Personalized Reason Generation for Explainable Song Recommendation

By Guoshuai Zhao, Hao Fu, Ruihua Song, Tetsuya Sakai, Zhongxia Chen, Xing Xie, Xueming Qian. 2019

53 citations recorded by the source corpus.

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as “Customers who bought this item also bought…”. Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as “Campus radio plays this song at noon every day, and I think it sounds wonderful,” which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations.

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. Xu Chen
2. Yongfeng Zhang
3. Dietmar Jannach
4. Li Chen
5. Wanling Cai
6. Ahtsham Manzoor
7. Xiangnan He
8. Zheng Qin

In Information Systems

1. Xu Chen
2. Yongfeng Zhang
3. Dietmar Jannach
4. Li Chen
5. Wanling Cai
6. Ahtsham Manzoor
7. Xiangnan He
8. Tat-seng Chua

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “Zhao et al. (2014) represented products and users in the same demographic feature space, and used the weights of the features learned by a ranking function to explain the results; Zhao et al.”
• A Survey on Conversational Recommender Systems (2020, methodology): “keywords (e.g., “iphone screen protector”) that are then used in the recommendation process to identify candidate items. Another supporting task in some applications is sentiment analysis, see, e.g., [54, 73, 95, 100, 165]. One typical goal in the context of CRS is to understand a user’s opinion abo”