{"type":"ScholarlyArticle","title":"Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback","authors":[{"name":"Fangyuan Luo","slug":"fangyuan-luo","given":"Fangyuan","family":"Luo","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing 100044 , China"},{"name":"Jun Wu","slug":"jun-wu","given":"Jun","family":"Wu","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing 100044 , China"},{"name":"Tao Wang","slug":"tao-wang","given":"Tao","family":"Wang","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing 100044 , China"}],"abstract":"We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N recommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learning objectives (such as pointwise or pairwise loss) with the benchmark metrics for ranking quality (e.g. Average Precision, AP), resulting in sub-optimal accuracy. To this end, we propose a Discrete Listwise Personalized Ranking (DLPR) model that optimizes AP under discrete constraints for fast and accurate top-N recommendation. To resolve the challenging DLPR problem, we devise an efficient algorithm that can directly learn binary codes in a relaxed continuous solution space. Specifically, theoretical analysis shows that the optimal solution to the relaxed continuous optimization problem is exactly the same as that of the original discrete DLPR problem. Through extensive experiments on two real-world datasets, we show that DLPR consistently surpasses state-of-the-art hashing methods for top-N recommendation.","doi":"10.24963/ijcai.2022/300","url":"https://ijcai.org/proceedings/2022/300","pdf":"https://ijcai.org/proceedings/2022/0300.pdf","markdown":"https://ijcai.org/proceedings/2022/300.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/300","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":300,"pages":{"first":2159,"last":2165},"keywords":["Data Mining: Recommender Systems"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986506","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984649","length_sec":292,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Brown et al., 2020] Andrew Brown, Weidi Xie, Vicky Kalogeiton, and Andrew Zisserman. Smooth-ap: Smooth- ing the path towards large-scale image retrieval. In ECCV, volume 12354, pages 677-694, 2020.","title":"Smooth-ap: Smoothing the path towards large-scale image retrieval","authors":"Andrew Brown, Weidi Xie, Vicky Kalogeiton, Andrew Zisserman","year":"2020","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Covington et al., 2016] Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recom- mendations. In RecSys, pages 191-198, 2016.","title":"Deep neural networks for youtube recommendations","authors":"Paul Covington, Jay Adams, Emre Sargin","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Grover et al., 2019] Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon. Stochastic optimization of sorting networks via continuous relaxations. In ICLR, 2019.","title":"Stochastic optimization of sorting networks via continuous relaxations","authors":"Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hansen et al., 2021] Christian Hansen, Casper Hansen, Jakob Grue Simonsen, and Christina Lioma. Projected hamming dissimilarity for bit-level importance coding in collaborative filtering. In WWW, pages 261-269, 2021.","title":"Projected hamming dissimilarity for bit-level importance coding in collaborative filtering","authors":"Hansen","year":"2021","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He et al., 2017] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collabo- rative filtering. In WWW, page 173-182, 2017.","title":"Neural collaborative filtering","authors":"He","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Koren et al., 2009] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recom- mender systems. Computer, 42(8):30-37, 2009.","title":"Matrix factorization techniques for recommender systems","authors":"Koren","year":"2009","venue":"Computer","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Li et al., 2021] Roger Zhe Li, Julián Urbano, and Alan Han- jalic. New insights into metric optimization for ranking- based recommendation. In SIGIR, pages 932-941, 2021.","title":"New insights into metric optimization for rankingbased recommendation","authors":"Li Roger Zhe, Julián Urbano, Alan Hanjalic","year":"2021","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Lian et al., 2021] Defu Lian, Xing Xie, and Enhong Chen. Discrete matrix factorization and extension for fast item recommendation. IEEE Trans. Knowl. Data Eng., 33(5):1919-1933, 2021.","title":"Discrete matrix factorization and extension for fast item recommendation","authors":"Defu Lian, Xing Xie, Enhong Chen","year":"2021","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liu and Qiao, 2014] Zhiyong Liu and Hong Qiao. GNCCP -graduated nonconvexityand concavity procedure. IEEE T. Pattern Anal., 36(6):1258-1267, 2014.","title":"GNCCP -graduated nonconvexityand concavity procedure","authors":"Zhiyong Liu, Hong Qiao","year":"2014","venue":"IEEE T. Pattern Anal","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Liu et al., 2019] Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, and Steven Hoi. Discrete social recom- mendation. In AAAI, 2019.","title":"Discrete social recommendation","authors":"Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven Hoi","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu et al., 2021] Chenghao Liu, Tao Lu, Zhiyong Cheng, Xin Wang, Jianling Sun, and Steven C. H. Hoi. Discrete listwise collaborative filtering for fast recommendation. In SDM, pages 46-54, 2021.","title":"Discrete listwise collaborative filtering for fast recommendation","authors":"Chenghao Liu, Tao Lu, Zhiyong Cheng, Xin Wang, Jianling Sun, Steven C H Hoi","year":"2021","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Luo et al., 2021] Fangyuan Luo, Jun Wu, and Haishuai Wang. Semi-discrete social recommendation (student ab- stract). In AAAI, pages 15835-15836, 2021.","title":"Semi-discrete social recommendation (student abstract)","authors":"Fangyuan Luo, Jun Wu, Haishuai Wang","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Marguerite Frank, 1956] Philip Wolfe Marguerite Frank. An algorithm for quadratic programming. Naval Research Logistics Quarterly, 3:95-110, 1956.","title":"An algorithm for quadratic programming","authors":"Philip Wolfe, Marguerite Frank","year":"1956","venue":"Naval Research Logistics Quarterly","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: bayesian personalized ranking from implicit feedback. In UAI, pages 452-461, 2009.","title":"BPR: bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Rockafellar, 1997] R.T. Rockafellar. Convex Analysis. Princeton Landmarks in Mathematics and Physics. Prince- ton University Press, 1997.","title":"Convex Analysis","authors":"R T Rockafellar","year":"1997","venue":"Princeton Landmarks in Mathematics and Physics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Shen et al., 2015] Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. Supervised discrete hashing. In CVPR, pages 37-45, 2015.","title":"Supervised discrete hashing","authors":"Shen","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2012] Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell., 34(12):2393- 2406, 2012.","title":"Semi-supervised hashing for large-scale search","authors":"Jun Wang, Sanjiv Kumar, Shih-Fu Chang","year":"2012","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Wang et al., 2016] J Wang, W Liu, S Kumar, and S Chang. Learning to hash for indexing big data -a survey. Proc. IEEE, 104(1):34-57, 2016.","title":"Learning to hash for indexing big data -a survey","authors":"W Wang, S Liu, Kumar, Chang","year":"2016","venue":"Proc. IEEE","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2018a] Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, and Heng Tao Shen. A survey on learn- ing to hash. IEEE T. Pattern Anal., 40(4):769-790, 2018.","title":"A survey on learning to hash","authors":"Wang","year":"2018","venue":"IEEE T. Pattern Anal","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2018b] Tao Wang, Haibin Ling, Congyan Lang, and Songhe Feng. Graph matching with adaptive and branching path following. IEEE T. Pattern Anal., 40(12):2853-2867, 2018.","title":"Graph matching with adaptive and branching path following","authors":"Wang","year":"2018","venue":"IEEE T. Pattern Anal","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2020] Tao Wang, He Liu, Yidong Li, Yi Jin, Xiaohui Hou, and Haibin Ling. Learning combinatorial solver for graph matching. In CVPR, pages 7565-7574, 2020.","title":"Learning combinatorial solver for graph matching","authors":"Tao Wang, He Liu, Yidong Li, Yi Jin, Xiaohui Hou, Haibin Ling","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wu et al., 2020] Jun Wu, Fangyuan Luo, Yujia Zhang, and Haishuai Wang. Semi-discrete matrix factorization. IEEE Intell. Syst., 35(5):73-83, 2020.","title":"Semi-discrete matrix factorization","authors":"Jun Wu, Fangyuan Luo, Yujia Zhang, Haishuai Wang","year":"2020","venue":"IEEE Intell. Syst","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhang et al., 2014] Zhiwei Zhang, Qifan Wang, Lingyun Ruan, and Luo Si. Preference preserving hashing for effi- cient recommendation. In SIGIR, pages 183-192, 2014.","title":"Preference preserving hashing for efficient recommendation","authors":"Zhiwei Zhang, Qifan Wang, Lingyun Ruan, Luo Si","year":"2014","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang et al., 2016] Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, and Tat Seng Chua. Discrete collaborative filtering. In SIGIR, pages 325-334, 2016.","title":"Discrete collaborative filtering","authors":"Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, Tat Seng, Chua","year":"2016","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhang et al., 2017] Yan Zhang, Defu Lian, and Guowu Yang. Discrete personalized ranking for fast collaborative filtering from implicit feedback. In AAAI, pages 1669- 1675, 2017.","title":"Discrete personalized ranking for fast collaborative filtering from implicit feedback","authors":"Yan Zhang, Defu Lian, Guowu Yang","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang et al., 2018a] Yan Zhang, Haoyu Wang, Defu Lian, Ivor W. Tsang, Hongzhi Yin, and Guowu Yang. Discrete ranking-based matrix factorization with self-paced learn- ing. In SIGKDD, pages 2758-2767, 2018.","title":"Discrete ranking-based matrix factorization with self-paced learning","authors":"Zhang","year":"2018","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang et al., 2018b] Yujia Zhang, Jun Wu, and Haishuai Wang. Binary collaborative filtering ensemble. In PRI- CAI, pages 993-1004, 2018.","title":"Binary collaborative filtering ensemble","authors":"Zhang","year":"2018","venue":"PRI-CAI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2019] Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. Deep learning based recommender system: A sur- vey and new perspectives. ACM Comput. Surv., 52(1):5:1- 5:38, 2019.","title":"Deep learning based recommender system: A survey and new perspectives","authors":"Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay","year":"2019","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang et al., 2020] Yan Zhang, Ivor W. Tsang, Hongzhi Yin, Guowu Yang, Defu Lian, and Jingjing Li. Deep pairwise hashing for cold-start recommendation. CoRR, abs/2011.00944, 2020.","title":"Deep pairwise hashing for cold-start recommendation","authors":"Yan Zhang, Ivor W Tsang, Hongzhi Yin, Guowu Yang, Defu Lian, Jingjing Li","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhou and Zha, 2012] Ke Zhou and Hongyuan Zha. Learn- ing binary codes for collaborative filtering. In SIGKDD, pages 498-506, 2012.","title":"Learning binary codes for collaborative filtering","authors":"Zha ; ] Ke Zhou, Hongyuan Zhou, Zha","year":"2012","venue":"SIGKDD","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}