{"type":"ScholarlyArticle","title":"Neural Re-ranking in Multi-stage Recommender Systems: A Review","authors":[{"name":"Weiwen Liu","slug":"weiwen-liu","given":"Weiwen","family":"Liu","affiliation":""},{"name":"Yunjia Xi","slug":"yunjia-xi","given":"Yunjia","family":"Xi","affiliation":"Shanghai Jiao Tong University"},{"name":"Jiarui Qin","slug":"jiarui-qin","given":"Jiarui","family":"Qin","affiliation":"Shanghai Jiao Tong University"},{"name":"Fei Sun","slug":"fei-sun","given":"Fei","family":"Sun","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Bo Chen","slug":"bo-chen","given":"Bo","family":"Chen","affiliation":""},{"name":"Weinan Zhang","slug":"weinan-zhang","given":"Weinan","family":"Zhang","affiliation":"Shanghai Jiao Tong University"},{"name":"Rui Zhang","slug":"rui-zhang","given":"Rui","family":"Zhang","affiliation":"Shanghai Jiao Tong University"},{"name":"Ruiming Tang","slug":"ruiming-tang","given":"Ruiming","family":"TANG","affiliation":""}],"abstract":"As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects users’ experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely adopted in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide a benchmark for the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available at https://github.com/LibRerank-Community/LibRerank.","doi":"10.24963/ijcai.2022/771","url":"https://ijcai.org/proceedings/2022/771","pdf":"https://ijcai.org/proceedings/2022/0771.pdf","markdown":"https://ijcai.org/proceedings/2022/771.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/771","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":771,"pages":{"first":5512,"last":5520},"keywords":["Survey Track: -"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987298","length_sec":79,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985441","length_sec":643,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abdool et al., 2020] Mustafa Abdool, Malay Haldar, Prashant Ramanathan, et al. 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