{"type":"ScholarlyArticle","title":"DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns","authors":[{"name":"Feng Yuan","slug":"feng-yuan","given":"Feng","family":"Yuan","affiliation":"University of New South Wales"},{"name":"Lina Yao","slug":"lina-yao","given":"Lina","family":"Yao","affiliation":"University of New South Wales"},{"name":"Boualem Benatallah","slug":"boualem-benatallah","given":"Boualem","family":"Benatallah","affiliation":"University of New South Wales"}],"abstract":"Cross-domain recommendation has long been one of the major topics in recommender systems.Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxilliary contents, e.g., images and review texts, and the patterns in the rating matrix itself is rarely touched. In this work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model (DARec) that is capable of extracting and transferring patterns from rating matrices only without relying on any auxillary information. We empirically demonstrate on public datasets that our method achieves the best performance among several state-of-the-art alternative cross-domain recommendation models.","doi":"10.24963/ijcai.2019/587","url":"https://ijcai.org/proceedings/2019/587","pdf":"https://ijcai.org/proceedings/2019/0587.pdf","markdown":"https://ijcai.org/proceedings/2019/587.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/587","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":587,"pages":{"first":4227,"last":4233},"keywords":["Machine Learning: Deep Learning","Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Berkovsky et al., 2007] Shlomo Berkovsky, Tsvi Kuflik, and Francesco Ricci. Cross-domain mediation in collabo- rative filtering. In International Conference on User Mod- eling, pages 355-359. Springer, 2007.","title":"Shlomo Berkovsky, Tsvi Kuflik, and Francesco Ricci. Cross-domain mediation in collaborative filtering","authors":"Berkovsky","year":"2007","venue":"International Conference on User Modeling","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bousmalis et al., 2016] Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Du- mitru Erhan. Domain separation networks. In Advances in Neural Information Processing Systems, pages 343-351, 2016.","title":"Domain separation networks","authors":"Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Elkahky et al., 2015] Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. A multi-view deep learning approach for cross domain user modeling in recommendation sys- tems. In Proceedings of the 24th International Conference on World Wide Web, pages 278-288. International World Wide Web Conferences Steering Committee, 2015.","title":"A multi-view deep learning approach for cross domain user modeling in recommendation systems","authors":"Ali Mamdouh, Elkahky, Yang Song, Xiaodong He","year":"2015","venue":"Proceedings of the 24th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ganin and Lempitsky, 2015] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backprop- agation. In International Conference on Machine Learn- ing, pages 1180-1189, 2015.","title":"Unsupervised domain adaptation by backpropagation","authors":"Yaroslav Ganin, Victor Lempitsky","year":"2015","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He and McAuley, 2016] Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceed- ings of the 25th international conference on world wide web, pages 507-517. International World Wide Web Con- ferences Steering Committee, 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":"proceedings of the 25th international conference on world wide web","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2017] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collab- orative filtering. In Proceedings of the 26th International Conference on World Wide Web, pages 173-182. Interna- tional World Wide Web Conferences Steering Committee, 2017.","title":"Neural collaborative filtering","authors":"He","year":"2017","venue":"Proceedings of the 26th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[He et al., 2018] Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, and Tat-Seng Chua. Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912, 2018.","title":"Outer product-based neural collaborative filtering","authors":"He","year":"2018","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2018/308"},{"position":8,"raw":"[Hu et al., 2013] Liang Hu, Jian Cao, Guandong Xu, Long- bing Cao, Zhiping Gu, and Can Zhu. Personalized recom- mendation via cross-domain triadic factorization. In Pro- ceedings of the 22nd international conference on World Wide Web, pages 595-606. ACM, 2013.","title":"Personalized recommendation via cross-domain triadic factorization","authors":"Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Can Zhu","year":"2013","venue":"Proceedings of the 22nd international conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hu, 2018] Herbert Hu. Conet: Collaborative cross net- works for cross-domain recommendation. arXiv preprint arXiv:1804.06769, 2018.","title":"Conet: Collaborative cross networks for cross-domain recommendation","authors":"Herbert Hu","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kanagawa et al., 2018] Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, and Taiji Suzuki. Cross-domain recommendation via deep domain adaptation. arXiv preprint arXiv:1803.03018, 2018.","title":"Cross-domain recommendation via deep domain adaptation","authors":"Kanagawa","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Khan et al., 2017] Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. Cross domain recommender systems: a systematic literature review. ACM Computing Surveys (CSUR), 50(3):36, 2017.","title":"Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. Cross domain recommender systems: a systematic literature review","authors":"Khan","year":"2017","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2009] Bin Li, Qiang Yang, and Xiangyang Xue. Transfer learning for collaborative filtering via a rating- matrix generative model. In Proceedings of the 26th an- nual international conference on machine learning, pages 617-624. ACM, 2009.","title":"Transfer learning for collaborative filtering via a ratingmatrix generative model","authors":"Li","year":"2009","venue":"Proceedings of the 26th annual international conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lian et al., 2017] Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. Cccfnet: a content-boosted collab- orative filtering neural network for cross domain recom- mender systems. In Proceedings of the 26th International Conference on World Wide Web Companion, pages 817- 818. International World Wide Web Conferences Steering Committee, 2017.","title":"Cccfnet: a content-boosted collaborative filtering neural network for cross domain recommender systems","authors":"Lian","year":"2017","venue":"Proceedings of the 26th International Conference on World Wide Web Companion","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Loni et al., 2014] Babak Loni, Yue Shi, Martha Larson, and Alan Hanjalic. Cross-domain collaborative filtering with factorization machines. In ECIR, 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":15,"raw":"[Mirbakhsh and Ling, 2015] Nima Mirbakhsh and Charles X Ling. Improving top-n recommendation for cold-start users via cross-domain information. ACM Transactions on Knowledge Discovery from Data (TKDD), 9(4):33, 2015.","title":"Nima Mirbakhsh and Charles X Ling. Improving top-n recommendation for cold-start users via cross-domain information","authors":"Ling Mirbakhsh","year":"2015","venue":"ACM Transactions on Knowledge Discovery from Data (TKDD)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mnih and Salakhutdinov, 2008] Andriy Mnih and Ruslan R Salakhutdinov. Probabilistic matrix factorization. In Ad- vances in neural information processing systems, pages 1257-1264, 2008.","title":"Probabilistic matrix factorization","authors":"Andriy Mnih, Ruslan R Salakhutdinov","year":"2008","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Salakhutdinov et al., 2007] Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. Restricted boltzmann ma- chines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning, pages 791-798. ACM, 2007.","title":"Restricted boltzmann machines for collaborative filtering","authors":"Salakhutdinov","year":"2007","venue":"Proceedings of the 24th international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Sedhain et al., 2015] Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. Autorec: Autoen- coders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web, pages 111-112. ACM, 2015.","title":"Autorec: Autoencoders meet collaborative filtering","authors":"Sedhain","year":"2015","venue":"Proceedings of the 24th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Singh and Gordon, 2008] Ajit P Singh and Geoffrey J Gor- don. Relational learning via collective matrix factoriza- tion. In Proceedings of the 14th ACM SIGKDD interna- tional conference on Knowledge discovery and data min- ing, pages 650-658. ACM, 2008.","title":"Relational learning via collective matrix factorization","authors":"P Ajit, Geoffrey J Singh, Gordon","year":"2008","venue":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zheng et al., 2016] Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. A neural autoregressive approach to collaborative filtering. arXiv preprint arXiv:1605.09477, 2016.","title":"A neural autoregressive approach to collaborative filtering","authors":"Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}