{"type":"ScholarlyArticle","title":"Cross-Domain Recommendation: An Embedding and Mapping Approach","authors":[{"name":"Tong Man","slug":"tong-man","given":"Tong","family":"Man","affiliation":"{CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , China}; {University of Chinese Academy of Sciences , China}"},{"name":"Huawei Shen","slug":"huawei-shen","given":"Huawei","family":"Shen","affiliation":"{CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , China}; {University of Chinese Academy of Sciences , China}"},{"name":"Xiaolong Jin","slug":"xiaolong-jin","given":"Xiaolong","family":"Jin","affiliation":"{CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , China}; {University of Chinese Academy of Sciences , China}"},{"name":"Xueqi Cheng","slug":"xueqi-cheng","given":"Xueqi","family":"Cheng","affiliation":"{CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , China}; {University of Chinese Academy of Sciences , China}"}],"abstract":"Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a collective manner. In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR. The proposed EMCDR framework distinguishes itself from existing cross-domain recommendation models in two aspects. First, a multi-layer perceptron is used to capture the nonlinear mapping function across domains, which offers high flexibility for learning domain-specific features of entities in each domain. Second, only the entities with sufficient data are used to learn the mapping function, guaranteeing its robustness to noise caused by data sparsity in single domain. Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms state-of-the-art cross-domain recommendation methods.","doi":"10.24963/ijcai.2017/343","url":"https://ijcai.org/proceedings/2017/343","pdf":"https://ijcai.org/proceedings/2017/0343.pdf","markdown":"https://ijcai.org/proceedings/2017/343.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/343","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":343,"pages":{"first":2464,"last":2470},"keywords":["Machine Learning: Data Mining","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Adomavicius and Tuzhilin, 2005] Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734-749, 2005.","title":"Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","authors":"Tuzhilin Adomavicius","year":"2005","venue":"Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Agarwal et al., 2011] Deepak Agarwal, Bee-Chung Chen, and Bo Long. Localized factor models for multi-context recommendation. In SIGKDD '11, 2011.","title":"Localized factor models for multi-context recommendation","authors":"Deepak Agarwal, Bee-Chung Chen, Bo Long","year":"2011","venue":"SIGKDD '11","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Glorot and Bengio, 2010] Xavier Glorot and Yoshua Ben- gio. Understanding the difficulty of training deep feed- forward neural networks. In AISTATS '10, 2010.","title":"Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks","authors":"Bengio Glorot","year":"2010","venue":"AISTATS '10","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Huang et al., 2012] Junming Huang, Xue-Qi Cheng, Hua- Wei Shen, Tao Zhou, and Xiaolong Jin. Exploring so- cial influence via posterior effect of word-of-mouth rec- ommendations. In WSDM '12, 2012.","title":"Exploring social influence via posterior effect of word-of-mouth recommendations","authors":"Junming Huang, Xue-Qi Cheng, Hua-Wei Shen, Tao Zhou, Xiaolong Jin","year":"2012","venue":"WSDM '12","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Jamali and Lakshmanan, 2013] Mohsen Jamali and Laks Lakshmanan. Heteromf: recommendation in heteroge- neous information networks using context dependent fac- tor models. In WWW '13, 2013.","title":"Heteromf: recommendation in heterogeneous information networks using context dependent factor models","authors":"Lakshmanan ; ] Mohsen Jamali, Laks Jamali, Lakshmanan","year":"2013","venue":"WWW '13","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":"[Le Cun et al., 1990] B Boser Le Cun, John S Denker, D Henderson, Richard E Howard, W Hubbard, and Lawrence D Jackel. Handwritten digit recognition with a back-propagation network. In NIPS '90, 1990.","title":"Handwritten digit recognition with a back-propagation network","authors":"B Boser, Le Cun, John S Denker, D Henderson, Richard E Howard, W Hubbard, Lawrence D Jackel","year":"1990","venue":"NIPS '90","doi":null,"url":null,"internal_url":null},{"position":8,"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 SIGKDD '14, 2014.","title":"Matching users and items across domains to improve the recommendation quality","authors":"Chung-Yi Li, Shou-De Lin","year":"2014","venue":"SIGKDD '14","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li et al., 2009a] Bin Li, Qiang Yang, and Xiangyang Xue. Can movies and books collaborate? cross-domain collabo- rative filtering for sparsity reduction. In IJCAI '09, 2009.","title":"Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction","authors":"Li","year":"2009","venue":"IJCAI '09","doi":null,"url":null,"internal_url":"/proceedings/2009/338"},{"position":10,"raw":"[Li et al., 2009b] Bin Li, Qiang Yang, and Xiangyang Xue. Transfer learning for collaborative filtering via a rating- matrix generative model. In ICML '09, 2009.","title":"Transfer learning for collaborative filtering via a ratingmatrix generative model","authors":"Li","year":"2009","venue":"ICML '09","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lin et al., 2013] Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, and Tat-Seng Chua. Addressing cold-start in app rec- ommendation: latent user models constructed from twitter followers. In SIGIR '13, 2013.","title":"Addressing cold-start in app recommendation: latent user models constructed from twitter followers","authors":"Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, Tat-Seng Chua","year":"2013","venue":"SIGIR '13","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2015] Yan-Fu Liu, CS NTHU, Cheng-Yu Hsu, Shan-Hung Wu, and NTHU EDU. Non-linear cross- domain collaborative filtering via hyper-structure transfer. In ICML '15, 2015.","title":"Non-linear crossdomain collaborative filtering via hyper-structure transfer","authors":"Yan-Fu Liu, C S Nthu, Cheng-Yu Hsu, Shan-Hung Wu, Nthu Edu","year":"2015","venue":"ICML '15","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Man et al., 2015] Tong Man, Hua-Wei Shen, Junming Huang, and Xue-Qi Cheng. Context-adaptive matrix fac- torization for multi-context recommendation. In CIKM '15, 2015.","title":"Context-adaptive matrix factorization for multi-context recommendation","authors":"Hua-Wei Tong Man, Junming Shen, Xue-Qi Huang, Cheng","year":"2015","venue":"CIKM '15","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Man et al., 2016] Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin, and Xueqi Cheng. Predict anchor links across social networks via an embedding approach. In IJ- CAI '16, 2016.","title":"Predict anchor links across social networks via an embedding approach","authors":"Huawei Tong Man, Shenghua Shen, Xiaolong Liu, Xueqi Jin, Cheng","year":"2016","venue":"IJ-CAI '16","doi":null,"url":null,"internal_url":"/proceedings/2016/261"},{"position":15,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Quoc V Le, and Ilya Sutskever. Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168, 2013.","title":"and Ilya Sutskever. Exploiting similarities among languages for machine translation","authors":"Mikolov","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mnih and Salakhutdinov, 2007] Andriy Mnih and Ruslan Salakhutdinov. Probabilistic matrix factorization. In NIPS '07, 2007.","title":"Probabilistic matrix factorization","authors":"Andriy Mnih, Ruslan Salakhutdinov","year":"2007","venue":"NIPS '07","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pan et al., 2010] Weike Pan, Evan Wei Xiang, Nathan Nan Liu, and Qiang Yang. Transfer learning in collaborative filtering for sparsity reduction. In AAAI '10, 2010.","title":"Transfer learning in collaborative filtering for sparsity reduction","authors":"Weike Pan, Evan Wei Xiang, Nathan, Nan Liu, Qiang Yang","year":"2010","venue":"AAAI '10","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Pan et al., 2011] Weike Pan, Nathan N Liu, Evan W Xiang, and Qiang Yang. Transfer learning to predict missing rat- ings via heterogeneous user feedbacks. In IJCAI '11, 2011.","title":"Transfer learning to predict missing ratings via heterogeneous user feedbacks","authors":"Weike Pan, Nathan N Liu, Evan W Xiang, Qiang Yang","year":"2011","venue":"IJCAI '11","doi":null,"url":null,"internal_url":"/proceedings/2011/386"},{"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 '09, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"UAI '09","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ruck et al., 1990] Dennis W Ruck, Steven K Rogers, Matthew Kabrisky, Mark E Oxley, and Bruce W Suter. The multilayer perceptron as an approximation to a bayes opti- mal discriminant function. Neural Networks, IEEE Trans- actions on, 1(4):296-298, 1990.","title":"The multilayer perceptron as an approximation to a bayes optimal discriminant function","authors":"Steven K Dennis W Ruck, Matthew Rogers, Mark E Kabrisky, Bruce W Oxley, Suter","year":"1990","venue":"Neural Networks","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sarwar et al., 2001] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filter- ing recommendation algorithms. In WWW '01, 2001.","title":"Item-based collaborative filtering recommendation algorithms","authors":"Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl","year":"2001","venue":"WWW '01","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Schein et al., 2002] Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. Methods and met- rics for cold-start recommendations. In SIGIR '02, 2002.","title":"Methods and metrics for cold-start recommendations","authors":"Alexandrin Andrew I Schein, Lyle H Popescul, David M Ungar, Pennock","year":"2002","venue":"SIGIR '02","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shen et al., 2014] Huawei Shen, Dashun Wang, Chaoming Song, and Albert-László Barabási. Modeling and predict- ing popularity dynamics via reinforced poisson processes. In AAAI '14, 2014.","title":"Modeling and predicting popularity dynamics via reinforced poisson processes","authors":"Shen","year":"2014","venue":"AAAI '14","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Singh and Gordon, 2008] Ajit P Singh and Geoffrey J Gor- don. Relational learning via collective matrix factoriza- tion. In SIGKDD '08, 2008.","title":"Relational learning via collective matrix factorization","authors":"P Ajit, Geoffrey J Singh, Gordon","year":"2008","venue":"SIGKDD '08","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhang et al., 2012] Yu Zhang, Bin Cao, and Dit-Yan Ye- ung. Multi-domain collaborative filtering. arXiv preprint arXiv:1203.3535, 2012.","title":"Multi-domain collaborative filtering","authors":"Yu Zhang, Bin Cao, Dit-Yan Yeung","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhou et al., 2011] Ke Zhou, Shuang-Hong Yang, and Hongyuan Zha. Functional matrix factorizations for cold- start recommendation. In SIGIR '11, 2011.","title":"Functional matrix factorizations for coldstart recommendation","authors":"Ke Zhou, Shuang-Hong Yang, Hongyuan Zha","year":"2011","venue":"SIGIR '11","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}