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Intell. Syst. Technol."],"published-print":{"date-parts":[[2019,7,31]]},"abstract":"<jats:p>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 \u201cCustomers who bought this item also bought\u2026\u201d. 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 \u201cCampus radio plays this song at noon every day, and I think it sounds wonderful,\u201d 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.<\/jats:p>","DOI":"10.1145\/3337967","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T12:10:48Z","timestamp":1562760648000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["Personalized Reason Generation for Explainable Song Recommendation"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4392-8450","authenticated-orcid":false,"given":"Guoshuai","family":"Zhao","sequence":"first","affiliation":[{"name":"Xi\u2019an Jiaotong University, Shannxi, China"}]},{"given":"Hao","family":"Fu","sequence":"additional","affiliation":[{"name":"Microsoft XiaoIce, Beijing, China"}]},{"given":"Ruihua","family":"Song","sequence":"additional","affiliation":[{"name":"Microsoft XiaoIce, Beijing, China"}]},{"given":"Tetsuya","family":"Sakai","sequence":"additional","affiliation":[{"name":"Waseda University, Tokyo, Japan"}]},{"given":"Zhongxia","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Xing","family":"Xie","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}]},{"given":"Xueming","family":"Qian","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Shannxi, China"}]}],"member":"320","published-online":{"date-parts":[[2019,7,10]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Explainable recommendation: A survey and new perspectives. 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