{"type":"ScholarlyArticle","title":"Learning Personalized Itemset Mapping for Cross-Domain Recommendation","authors":[{"name":"Yinan Zhang","slug":"yinan-zhang","given":"Yinan","family":"Zhang","affiliation":"Alibaba-NTU Singapore Joint Research Institute; School of Computer Science and Engineering , Nanyang Technological University"},{"name":"Yong Liu","slug":"yong-liu","given":"Yong","family":"Liu","affiliation":"Alibaba-NTU Singapore Joint Research Institute; Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)"},{"name":"Peng Han","slug":"peng-han","given":"Peng","family":"Han","affiliation":"King Abdullah University of Science and Technology; Alibaba Group"},{"name":"Chunyan Miao","slug":"chunyan-miao","given":"Chunyan","family":"Miao","affiliation":"School of Computer Science and Engineering , Nanyang Technological University"},{"name":"Lizhen Cui","slug":"lizhen-cui","given":"Lizhen","family":"Cui","affiliation":"School of Software & Joint SDU - NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University"},{"name":"Baoli Li","slug":"baoli-li","given":"Baoli","family":"Li","affiliation":"Alibaba Group"},{"name":"Haihong Tang","slug":"haihong-tang","given":"Haihong","family":"Tang","affiliation":"Alibaba Group"}],"abstract":"Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.","doi":"10.24963/ijcai.2020/355","url":"https://ijcai.org/proceedings/2020/355","pdf":"https://ijcai.org/proceedings/2020/0355.pdf","markdown":"https://ijcai.org/proceedings/2020/355.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/355","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":355,"pages":{"first":2561,"last":2567},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24417/file_24417.m3u8","length_sec":262,"provider":"hls"}],"references":[{"position":1,"raw":"[Fu et al., 2019] Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, and Jin Li. Deeply fusing reviews and contents for cold start users in cross-domain recommenda- tion systems. In AAAI, pages 94-101, 2019.","title":"Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems","authors":"Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, Jin Li","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[He and McAuley, 2016] Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fash- ion trends with one-class collaborative filtering. In WWW, pages 507-517, 2016.","title":"Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering","authors":"Mcauley He","year":"2016","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Hu et al., 2018] Guangneng Hu, Yu Zhang, and Qiang Yang. Conet: Collaborative cross networks for cross- domain recommendation. In CIKM, pages 667-676, 2018.","title":"Conet: Collaborative cross networks for crossdomain recommendation","authors":"Guangneng Hu, Yu Zhang, Qiang Yang","year":"2018","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hu et al., 2019] Guangneng Hu, Yu Zhang, and Qiang Yang. Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text. In WWW, pages 2822-2829, 2019.","title":"Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text","authors":"Guangneng Hu, Yu Zhang, Qiang Yang","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Huang et al., 2019] Ling Huang, Zhi-Lin Zhao, Chang- Dong Wang, Dong Huang, and Hong-Yang Chao. Lscd: Low-rank and sparse cross-domain recommendation. Neu- rocomputing, 366:86-96, 2019.","title":"Lscd: Low-rank and sparse cross-domain recommendation","authors":"Ling Huang, Zhi-Lin Zhao, Chang-Dong Wang, Dong Huang, Hong-Yang Chao","year":"2019","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Jiang et al., 2016] Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, and Shiqiang Yang. Little is much: bridg- ing cross-platform behaviors through overlapped crowds. In AAAI, pages 13-19, 2016.","title":"Little is much: bridging cross-platform behaviors through overlapped crowds","authors":"Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, Shiqiang Yang","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kanagawa et al., 2019] Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, and Taiji Suzuki. Cross-domain recommendation via deep domain adaptation. In ECIR, pages 20-29, 2019.","title":"Cross-domain recommendation via deep domain adaptation","authors":"Kanagawa","year":"2019","venue":"ECIR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kingma and Ba, 2015] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li and Lin, 2014] Chung-Yi Li and Shou-De Lin. Matching users and items across domains to improve the recommen- dation quality. In KDD, pages 801-810, 2014.","title":"Matching users and items across domains to improve the recommendation quality","authors":"Chung-Yi Li, Shou-De Lin","year":"2014","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li and Tuzhilin, 2020] Pan Li and Alexander Tuzhilin. Ddtcdr: Deep dual transfer cross domain recommendation. In WSDM, pages 331-339, 2020.","title":"Ddtcdr: Deep dual transfer cross domain recommendation","authors":"Tuzhilin ; Li, Pan Li, Alexander Tuzhilin","year":"2020","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lian et al., 2020a] Defu Lian, Qi Liu, and Enhong Chen. Personalized ranking with importance sampling. In WWW, pages 1093-1103, 2020.","title":"Personalized ranking with importance sampling","authors":"Lian","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lian et al., 2020b] Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen, and Xing Xie. Lightrec: A memory and search-efficient recommender system. In WWW, pages 695-705, 2020.","title":"Lightrec: A memory and search-efficient recommender system","authors":"Lian","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2015] Yan-Fu Liu, Cheng-Yu Hsu, and Shan- Hung Wu. Non-linear cross-domain collaborative filtering via hyper-structure transfer. In ICML, pages 1190-1198, 2015.","title":"Non-linear cross-domain collaborative filtering via hyper-structure transfer","authors":"Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2017] Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Lixin Duan, and Xiao-Li Li. Learning user dependencies for recommendation. In IJCAI, pages 2379-2385, 2017.","title":"Learning user dependencies for recommendation","authors":"Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Lixin Duan, Xiao-Li Li","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/331"},{"position":15,"raw":"[Liu et al., 2018] Yong Liu, Lifan Zhao, Guimei Liu, Xinyan Lu, Peng Gao, Xiao-Li Li, and Zhihui Jin. Dynamic bayesian logistic matrix factorization for recommendation with implicit feedback. In IJCAI, pages 3463-3469, 2018.","title":"Dynamic bayesian logistic matrix factorization for recommendation with implicit feedback","authors":"Yong Liu, Lifan Zhao, Guimei Liu, Xinyan Lu, Peng Gao, Xiao-Li Li, Zhihui Jin","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/481"},{"position":16,"raw":"[Loni et al., 2014] Babak Loni, Yue Shi, Martha Larson, and Alan Hanjalic. Cross-domain collaborative filtering with factorization machines. In ECIR, pages 656-661, 2014.","title":"Cross-domain collaborative filtering with factorization machines","authors":"Babak Loni, Yue Shi, Martha Larson, Alan Hanjalic","year":"2014","venue":"ECIR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Man et al., 2017] Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. Cross-domain recommendation: An embedding and mapping approach. In IJCAI, pages 2464- 2470, 2017.","title":"Cross-domain recommendation: An embedding and mapping approach","authors":"Huawei Tong Man, Xiaolong Shen, Xueqi Jin, Cheng","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/343"},{"position":18,"raw":"[Pan, 2016] Weike Pan. A survey of transfer learning for collaborative recommendation with auxiliary data. Neu- rocomputing, 177:447-453, 2016.","title":"A survey of transfer learning for collaborative recommendation with auxiliary data","authors":"Weike Pan, Pan","year":"2016","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":19,"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":20,"raw":"[Singh and Gordon, 2008] Ajit P Singh and Geoffrey J Gor- don. Relational learning via collective matrix factoriza- tion. In KDD, pages 650-658, 2008.","title":"Relational learning via collective matrix factorization","authors":"P Ajit, Geoffrey J Singh, Gordon","year":"2008","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tang and Wang, 2018] Jiaxi Tang and Ke Wang. Person- alized top-n sequential recommendation via convolutional sequence embedding. In WSDM, pages 565-573, 2018.","title":"Personalized top-n sequential recommendation via convolutional sequence embedding","authors":"Wang ; ] Jiaxi Tang, Ke Tang, Wang","year":"2018","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2017] Shoujin Wang, Liang Hu, and Longbing Cao. Perceiving the next choice with comprehensive trans- action embeddings for online recommendation. In ECML- PKDD, pages 285-302, 2017.","title":"Perceiving the next choice with comprehensive transaction embeddings for online recommendation","authors":"Wang","year":"2017","venue":"ECML-PKDD","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2019] Shoujin Wang, Liang Hu, Yan Wang, Quan Z Sheng, Mehmet Orgun, and Longbing Cao. Mod- eling multi-purpose sessions for next-item recommenda- tions via mixture-channel purpose routing networks. In IJCAI, pages 1-7, 2019.","title":"Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks","authors":"Shoujin Wang, Liang Hu, Yan Wang, Z Quan, Mehmet Sheng, Longbing Orgun, Cao","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/523"},{"position":24,"raw":"[Wu et al., 2019] Qiong Wu, Yong Liu, Chunyan Miao, Bin- qiang Zhao, Yin Zhao, and Lu Guan. Pd-gan: adversarial learning for personalized diversity-promoting recommen- dation. In IJCAI, pages 3870-3876, 2019.","title":"Pd-gan: adversarial learning for personalized diversity-promoting recommendation","authors":"Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/537"},{"position":25,"raw":"[Yuan et al., 2019] Feng Yuan, Lina Yao, and Boualem Be- natallah. Darec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns. IJCAI, pages 4227-4233, 2019.","title":"Darec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns","authors":"Feng Yuan, Lina Yao, Boualem Benatallah","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/587"},{"position":26,"raw":"[Yujia Li, 2015] Richard Zemel Yujia Li, Kevin Swersky. Generative moment matching networks. In ICML, pages 1718-1727, 2015.","title":"Generative moment matching networks","authors":"Richard Zemel, Yujia Li, Kevin Swersky","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang et al., 2017] Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, and Lawrence Carin. Adversarial feature matching for text generation. In ICML, pages 4006-4015, 2017.","title":"Adversarial feature matching for text generation","authors":"Zhang","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhu et al., 2017] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image transla- tion using cycle-consistent adversarial networks. In ICCV, pages 2223-2232, 2017.","title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","authors":"Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A Efros","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhu et al., 2018] Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, and Jia Wu. A deep framework for cross-domain and cross-system recommen- dations. In IJCAI, pages 3711-3717, 2018.","title":"A deep framework for cross-domain and cross-system recommendations","authors":"Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, Jia Wu","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/516"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}