{"type":"ScholarlyArticle","title":"Deep Feedback Network for Recommendation","authors":[{"name":"Ruobing Xie","slug":"ruobing-xie","given":"Ruobing","family":"Xie","affiliation":"WeChat Search Application Department , Tencent , China"},{"name":"Cheng Ling","slug":"cheng-ling","given":"Cheng","family":"Ling","affiliation":"WeChat Search Application Department , Tencent , China"},{"name":"Yalong Wang","slug":"yalong-wang","given":"Yalong","family":"Wang","affiliation":"WeChat Search Application Department , Tencent , China"},{"name":"Rui Wang","slug":"rui-wang","given":"Rui","family":"Wang","affiliation":"WeChat Search Application Department , Tencent , China"},{"name":"Feng Xia","slug":"feng-xia","given":"Feng","family":"Xia","affiliation":"WeChat Search Application Department , Tencent , China"},{"name":"Leyu Lin","slug":"leyu-lin","given":"Leyu","family":"Lin","affiliation":"WeChat Search Application Department , Tencent , China"}],"abstract":"Both explicit and implicit feedbacks can reflect user opinions on items, which are essential for learning user preferences in recommendation. However, most current recommendation algorithms merely focus on implicit positive feedbacks (e.g., click), ignoring other informative user behaviors. In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation. Specifically, we propose a novel Deep feedback network (DFN) modeling click, unclick and dislike behaviors. DFN has an internal feedback interaction component that captures fine-grained interactions between individual behaviors, and an external feedback interaction component that uses precise but relatively rare feedbacks (click/dislike) to extract useful information from rich but noisy feedbacks (unclick). In experiments, we conduct both offline and online evaluations on a real-world recommendation system WeChat Top Stories used by millions of users. The significant improvements verify the effectiveness and robustness of DFN. The source code is in https://github.com/qqxiaochongqq/DFN.","doi":"10.24963/ijcai.2020/349","url":"https://ijcai.org/proceedings/2020/349","pdf":"https://ijcai.org/proceedings/2020/0349.pdf","markdown":"https://ijcai.org/proceedings/2020/349.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/349","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":349,"pages":{"first":2519,"last":2525},"keywords":["Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24234/file_24234.m3u8","length_sec":789,"provider":"hls"}],"references":[{"position":1,"raw":"[Cheng et al., 2016] Heng-Tze Cheng, Levent Koc, Jeremi- ah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 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