{"type":"ScholarlyArticle","title":"Domain Adaptation via Tree Kernel Based Maximum Mean Discrepancy for User Consumption Intention Identification","authors":[{"name":"Xiao Ding","slug":"xiao-ding","given":"Xiao","family":"Ding","affiliation":"Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Bibo Cai","slug":"bibo-cai","given":"Bibo","family":"Cai","affiliation":"Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":"Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Qiankun Shi","slug":"qiankun-shi","given":"Qiankun","family":"Shi","affiliation":"Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"}],"abstract":"Identifying user consumption intention from social media is of great interests to downstream applications. Since such task is domain-dependent, deep neural networks have been applied to learn transferable features for adapting models from a source domain to a target domain. A basic idea to solve this problem is reducing the distribution difference between the source domain and the target domain such that the transfer error can be bounded. However, the feature transferability drops dramatically in higher layers of deep neural networks with increasing domain discrepancy. Hence, previous work has to use a few target domain annotated data to train domain-specific layers. In this paper, we propose a deep transfer learning framework for consumption intention identification, to reduce the data bias and enhance the transferability in domain-specific layers. In our framework, the representation of the domain-specific layer is mapped to a reproducing kernel Hilbert space, where the mean embeddings of different domain distributions can be explicitly matched. By using an optimal tree kernel method for measuring the mean embedding matching, the domain discrepancy can be effectively reduced. The framework can learn transferable features in a completely unsupervised manner with statistical guarantees. Experimental results on five different domain datasets show that our approach dramatically outperforms state-of-the-art baselines, and it is general enough to be applied to more scenarios. The source code and datasets can be found at http://ir.hit.edu.cn/$\\scriptsize{\\sim}$xding/index\\_english.htm.","doi":"10.24963/ijcai.2018/560","url":"https://ijcai.org/proceedings/2018/560","pdf":"https://ijcai.org/proceedings/2018/0560.pdf","markdown":"https://ijcai.org/proceedings/2018/560.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/560","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":"Natural Language Processing","track":"Main track","number":560,"pages":{"first":4026,"last":4032},"keywords":["Multidisciplinary Topics and Applications: Social Sciences","Natural Language Processing: Information Extraction","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Agarwal and Sureka, 2017] Swati Agarwal and Ashish Sureka. Characterizing linguistic attributes for automatic classification of intent based racist/radicalized posts on tumblr micro-blogging website. arXiv preprint, 2017.","title":"Characterizing linguistic attributes for automatic classification of intent based racist/radicalized posts on tumblr micro-blogging website","authors":"Sureka Agarwal, Swati Agarwal, Ashish Sureka","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ben-David et al., 2007] Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. Analysis of representations for domain adaptation. In NIPS, 2007.","title":"Analysis of representations for domain adaptation","authors":"Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira","year":"2007","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bengio et al., 2013] Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelli- gence, IEEE Transactions on, 2013.","title":"Representation learning: A review and new perspectives","authors":"Yoshua Bengio, Aaron Courville, Pascal Vincent","year":"2013","venue":"Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Blitzer et al., 2007] John Blitzer, Mark Dredze, Fernando Pereira, et al. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, 2007.","title":"Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification","authors":"John Blitzer, Mark Dredze, Fernando Pereira","year":"2007","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Borgwardt et al., 2006] Karsten M Borgwardt, Arthur Gret- ton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola. Integrating structured bio- logical data by kernel maximum mean discrepancy. Bioin- formatics, 2006.","title":"Integrating structured biological data by kernel maximum mean discrepancy","authors":"Karsten M Borgwardt, Arthur Gretton, J Malte, Hans-Peter Rasch, Bernhard Kriegel, Alex J Schölkopf, Smola","year":"2006","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cao et al., 2013] Xudong Cao, David Wipf, Fang Wen, Genquan Duan, and Jian Sun. A practical transfer learning algorithm for face verification. In ICCV, 2013.","title":"A practical transfer learning algorithm for face verification","authors":"Xudong Cao, David Wipf, Fang Wen, Genquan Duan, Jian Sun","year":"2013","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cohen, 1968] Jacob Cohen. Weighted kappa: Nominal scale agreement provision for scaled disagreement or par- tial credit. Psychological bulletin, 1968.","title":"Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit","authors":"Jacob Cohen","year":"1968","venue":"Psychological bulletin","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Culotta and Sorensen, 2004] Aron Culotta and Jeffrey Sorensen. Dependency tree kernels for relation extraction. In ACL, 2004.","title":"Dependency tree kernels for relation extraction","authors":"Aron Culotta, Jeffrey Sorensen","year":"2004","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dai et al., 2006] Honghua Kathy Dai, Lingzhi Zhao, Za- iqing Nie, Ji-Rong Wen, Lee Wang, and Ying Li. Detect- ing online commercial intention (oci). In WWW, 2006.","title":"Detecting online commercial intention (oci)","authors":"Kathy Honghua, Lingzhi Dai, Zaiqing Zhao, Ji-Rong Nie, Lee Wen, Ying Wang, Li","year":"2006","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ding et al., 2015] Xiao Ding, Ting Liu, Junwen Duan, and Jian-Yun Nie. Mining user consumption intention from social media using domain adaptive convolutional neural network. In AAAI, 2015.","title":"Mining user consumption intention from social media using domain adaptive convolutional neural network","authors":"Xiao Ding, Ting Liu, Junwen Duan, Jian-Yun Nie","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ganin and Lempitsky, 2015] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backprop- agation. In ICML, 2015.","title":"Unsupervised domain adaptation by backpropagation","authors":"Yaroslav Ganin, Victor Lempitsky","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gretton et al., 2012] Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. A kernel two-sample test. JMLR, 2012.","title":"A kernel two-sample test","authors":"Arthur Gretton, Karsten M Borgwardt, J Malte, Bernhard Rasch, Alexander Schölkopf, Smola","year":"2012","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hollerit et al., 2013] Bernd Hollerit, Mark Kröll, and Markus Strohmaier. Towards linking buyers and sellers: detecting commercial intent on twitter. In WWW, 2013.","title":"Towards linking buyers and sellers: detecting commercial intent on twitter","authors":"Bernd Hollerit, Mark Kröll, Markus Strohmaier","year":"2013","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2007] Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola. Correcting sample selection bias by unlabeled data. In NIPS, 2007.","title":"Correcting sample selection bias by unlabeled data","authors":"Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, Alex J Smola","year":"2007","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[LeCun et al., 2015] Yann LeCun, Yoshua Bengio, and Ge- offrey Hinton. Deep learning. Nature, 2015.","title":"Deep learning","authors":"Yann Lecun, Yoshua Bengio, Geoffrey Hinton","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lo et al., 2016] Caroline Lo, Dan Frankowski, and Jure Leskovec. Understanding behaviors that lead to purchas- ing: A case study of pinterest. In KDD, 2016.","title":"Understanding behaviors that lead to purchasing: A case study of pinterest","authors":"Lo","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Long et al., 2015] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In ICML, 2015.","title":"Learning transferable features with deep adaptation networks","authors":"Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Long et al., 2016] Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, and S Yu Philip. Deep learning of transferable representation for scalable domain adaptation. TKDE, 2016.","title":"Deep learning of transferable representation for scalable domain adaptation","authors":"Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, S Yu, Philip","year":"2016","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Long et al., 2017] Mingsheng Long, Jianmin Wang, and Michael I Jordan. Deep transfer learning with joint adap- tation networks. 2017.","title":"Deep transfer learning with joint adaptation networks","authors":"Mingsheng Long, Jianmin Wang, Michael I Jordan","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Oquab et al., 2014] Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. Learning and transferring mid- level image representations using convolutional neural net- works. In CVPR, 2014.","title":"Learning and transferring midlevel image representations using convolutional neural networks","authors":"Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic","year":"2014","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Pan et al., 2008] Sinno Jialin Pan, James T Kwok, and Qiang Yang. Transfer learning via dimensionality reduc- tion. In AAAI, 2008.","title":"Transfer learning via dimensionality reduction","authors":"James T Sinno Jialin Pan, Qiang Kwok, Yang","year":"2008","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Pan et al., 2010] Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. Cross-domain sen- timent classification via spectral feature alignment. In WWW, 2010.","title":"Cross-domain sentiment classification via spectral feature alignment","authors":"Xiaochuan Sinno Jialin Pan, Jian-Tao Ni, Qiang Sun, Zheng Yang, Chen","year":"2010","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Si et al., 2010] Si Si, Dacheng Tao, and Bo Geng. Breg- man divergence-based regularization for transfer subspace learning. TKDE, 2010.","title":"Bregman divergence-based regularization for transfer subspace learning","authors":"Si Si, Dacheng Tao, Bo Geng","year":"2010","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Steinwart and Christmann, 2008] Ingo Steinwart and An- dreas Christmann. Support vector machines. Springer Sci- ence & Business Media, 2008.","title":"Ingo Steinwart and Andreas Christmann. Support vector machines","authors":"Christmann Steinwart","year":"2008","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tai et al., 2015] Kai Sheng Tai, Richard Socher, and Christopher D Manning. Improved semantic representa- tions from tree-structured long short-term memory net- works. In ACL, 2015.","title":"Improved semantic representations from tree-structured long short-term memory networks","authors":"Kai Sheng, Tai, Richard Socher, Christopher D Manning","year":"2015","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2013] Jinpeng Wang, Wayne Xin Zhao, Haitian Wei, Hongfei Yan, and Xiaoming Li. Mining new business opportunities: Identifying trend related products by lever- aging commercial intents from microblogs. In EMNLP, 2013.","title":"Mining new business opportunities: Identifying trend related products by leveraging commercial intents from microblogs","authors":"Jinpeng Wang, Wayne Xin Zhao, Haitian Wei, Hongfei Yan, Xiaoming Li","year":"2013","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2015] Jinpeng Wang, Gao Cong, Wayne Xin Zhao, and Xiaoming Li. Mining user intents in twitter: A semi-supervised approach to inferring intent categories for tweets. In AAAI, 2015.","title":"Mining user intents in twitter: A semi-supervised approach to inferring intent categories for tweets","authors":"Jinpeng Wang, Gao Cong, Wayne Xin Zhao, Xiaoming Li","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yang and Li, 2013] Huahai Yang and Yunyao Li. Identify- ing user needs from social media. IBM Research Division, San Jose, 2013.","title":"Identifying user needs from social media","authors":"Yang, Li ;, Huahai Yang, Yunyao Li","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Yosinski et al., 2014] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In NIPS, 2014.","title":"How transferable are features in deep neural networks?","authors":"Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhao et al., 2014] Xin Wayne Zhao, Yanwei Guo, Yulan He, Han Jiang, Yuexin Wu, and Xiaoming Li. We know what you want to buy: a demographic-based system for product recommendation on microblogs. In KDD, 2014.","title":"We know what you want to buy: a demographic-based system for product recommendation on microblogs","authors":"Xin Wayne Zhao, Yanwei Guo, Yulan He, Han Jiang, Yuexin Wu, Xiaoming Li","year":"2014","venue":"KDD","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}