{"type":"ScholarlyArticle","title":"Discrete Factorization Machines for Fast Feature-based Recommendation","authors":[{"name":"Han Liu","slug":"han-liu","given":"Han","family":"Liu","affiliation":"School of Computer Science and Technology , Shandong University"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"School of Computing , National University of Singapore"},{"name":"Fuli Feng","slug":"fuli-feng","given":"Fuli","family":"Feng","affiliation":"School of Computing , National University of Singapore"},{"name":"Liqiang Nie","slug":"liqiang-nie","given":"Liqiang","family":"Nie","affiliation":"School of Computer Science and Technology , Shandong University"},{"name":"Rui Liu","slug":"rui-liu","given":"Rui","family":"Liu","affiliation":"University of Electronic Science and Technology of China"},{"name":"Hanwang Zhang","slug":"hanwang-zhang","given":"Hanwang","family":"Zhang","affiliation":"School of Computer Science and Engineering , Nanyang Technological University"}],"abstract":"User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 107, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM.   Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss.","doi":"10.24963/ijcai.2018/479","url":"https://ijcai.org/proceedings/2018/479","pdf":"https://ijcai.org/proceedings/2018/0479.pdf","markdown":"https://ijcai.org/proceedings/2018/479.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/479","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":479,"pages":{"first":3449,"last":3455},"keywords":["Machine Learning: Machine Learning","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2017] Ting Bai, Ji-Rong Wen, Jun Zhang, and Wayne Xin Zhao. 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