{"type":"ScholarlyArticle","title":"HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation","authors":[{"name":"Fan Wang","slug":"fan-wang","given":"Fan","family":"Wang","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Weiming Liu","slug":"weiming-liu","given":"Weiming","family":"Liu","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Chaochao Chen","slug":"chaochao-chen","given":"Chaochao","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Mengying Zhu","slug":"mengying-zhu","given":"Mengying","family":"Zhu","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Xiaolin Zheng","slug":"xiaolin-zheng","given":"Xiaolin","family":"Zheng","affiliation":"College of Computer Science and Technology , Zhejiang University , China"}],"abstract":"The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-CF) approaches employ efficient Hamming distance of learned binary representations of users and items to accelerate recommendations. However, Hash-CF often faces two challenging problems, i.e., optimization on discrete representations and preserving semantic information in learned representations. To address the above two challenges, we propose HCFRec, a novel Hash-CF approach for effective and efficient recommendations. Specifically, HCFRec not only innovatively introduces normalized flow to learn the optimal hash code by efficiently fitting a proposed approximate mixture  multivariate  normal distribution, a continuous but approximately discrete distribution, but also deploys a cluster consistency preserving mechanism to preserve the semantic structure in representations for more accurate recommendations. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our HCFRec compared to the state-of-art methods in terms of effectiveness and efficiency.","doi":"10.24963/ijcai.2022/315","url":"https://ijcai.org/proceedings/2022/315","pdf":"https://ijcai.org/proceedings/2022/0315.pdf","markdown":"https://ijcai.org/proceedings/2022/315.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/315","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":"Data Mining","track":"Main Track","number":315,"pages":{"first":2270,"last":2276},"keywords":["Data Mining: Recommender Systems"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986608","length_sec":78,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984751","length_sec":235,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chen et al., 2018] Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, and Xiaolong Li. 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