Personalized Reason Generation for Explainable Song Recommendation
| Autor: | Zhongxia Chen, Tetsuya Sakai, Xueming Qian, Ruihua Song, Xing Xie, Guoshuai Zhao, Hao Fu |
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| Rok vydání: | 2019 |
| Předmět: |
Statement (computer science)
Computer science media_common.quotation_subject Natural language generation 02 engineering and technology Recommender system Theoretical Computer Science Personalization World Wide Web Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Conversation Relevance (information retrieval) Natural language media_common |
| Zdroj: | ACM Transactions on Intelligent Systems and Technology. 10:1-21 |
| ISSN: | 2157-6912 2157-6904 |
| DOI: | 10.1145/3337967 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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