{"type":"ScholarlyArticle","title":"A Social Interaction Activity based Time-Varying User Vectorization Method for Online Social Networks","authors":[{"name":"Tianyi Hao","slug":"tianyi-hao","given":"Tianyi","family":"Hao","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University"},{"name":"Longbo Huang","slug":"longbo-huang","given":"Longbo","family":"Huang","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University"}],"abstract":"In this paper, we consider the problem of user modeling in online social networks, and propose a social interaction activity based user vectorization framework, called the time-varying user vectorization (Tuv), to infer and make use of important user features. Tuv is designed based on a novel combination of word2vec, negative sampling and a smoothing technique for model training. It jointly handles multi-format user data and computes user representing vectors, by taking into consideration user feature variation, self-similarity and pairwise interactions among users. The framework enables us to extract hidden user properties and to produce user vectors. We conduct extensive experiments based on a real-world dataset, which show that Tuv significantly outperforms several state-of-the-art user vectorization methods.","doi":"10.24963/ijcai.2018/527","url":"https://ijcai.org/proceedings/2018/527","pdf":"https://ijcai.org/proceedings/2018/0527.pdf","markdown":"https://ijcai.org/proceedings/2018/527.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/527","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":"Multidisciplinary Topics and Applications","track":"Main track","number":527,"pages":{"first":3790,"last":3796},"keywords":["Multidisciplinary Topics and Applications: AI and the Web","Natural Language Processing: Embeddings","Humans and AI: Personalization and User Modeling","Knowledge Representation and Reasoning: Information Fusion","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Adi et al., 2017] Yossi Adi, Einat Kermany, Yonatan Be- linkov, Ofer Lavi, and Yoav Goldberg. Fine-grained anal- ysis of sentence embeddings using auxiliary prediction tasks. In ICLR, 2017.","title":"Fine-grained analysis of sentence embeddings using auxiliary prediction tasks","authors":"Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, Yoav Goldberg","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Amir et al., 2016] Silvio Amir, Byron C Wallace, Hao Lyu, and Paula Carvalho Mário J Silva. Modelling context with user embeddings for sarcasm detection in social media. In CoNLL, 2016.","title":"Modelling context with user embeddings for sarcasm detection in social media","authors":"Silvio Amir, Byron C Wallace, Hao Lyu, Paula Carvalho Mário, J Silva","year":"2016","venue":"CoNLL","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Arora et al., 2015] Piyush Arora, Chris Hokamp, Jennifer Foster, and Gareth JF Jones. DCU: Using distributional semantics and domain adaptation for the semantic textual similarity SemEval-2015 Task 2. In SemEval@NAACL- HLT, pages 143-147, 2015.","title":"DCU: Using distributional semantics and domain adaptation for the semantic textual similarity SemEval-2015 Task 2","authors":"Piyush Arora, Chris Hokamp, Jennifer Foster, Gareth, Jones","year":"2015","venue":"SemEval@NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bottou, 1991] Léon Bottou. Stochastic gradient learning in neural networks. Neuro-Nımes, 91(8):0, 1991.","title":"Stochastic gradient learning in neural networks","authors":"Léon Bottou, Bottou","year":"1991","venue":"Neuro-Nımes","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chang and Lin, 2011] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines. TIST, 2(3):27, 2011.","title":"LIBSVM: a library for support vector machines","authors":"Chang, Lin ; ] Chih-Chung Chang, Chih-Jen Lin","year":"2011","venue":"TIST","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cho et al., 2016] Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, and Aram Galstyan. Latent space model for multi- modal social data. In WWW, pages 447-458, 2016.","title":"Latent space model for multimodal social data","authors":"Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, Aram Galstyan","year":"2016","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cortes and Vapnik, 1995] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.","title":"Corinna Cortes and Vladimir Vapnik. Support-vector networks","authors":"Vapnik Cortes","year":"1995","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Coulom, 2008] Rémi Coulom. Whole-history rating: A Bayesian rating system for players of time-varying strength. In International Conference on Computers and Games, pages 113-124. Springer, 2008.","title":"Rémi Coulom. Whole-history rating: A Bayesian rating system for players of time-varying strength","authors":"Coulom","year":"2008","venue":"International Conference on Computers and Games","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fu et al., 2009] Wenjie Fu, Le Song, and Eric P Xing. Dy- namic mixed membership blockmodel for evolving net- works. In ICML, pages 329-336. ACM, 2009.","title":"Dynamic mixed membership blockmodel for evolving networks","authors":"Wenjie Fu, Le Song, Eric P Xing","year":"2009","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In KDD, pages 855-864. ACM, 2016.","title":"Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks","authors":"Leskovec Grover","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Le and Mikolov, 2014] Quoc Le and Tomas Mikolov. Dis- tributed representations of sentences and documents. In ICML, pages 1188-1196, 2014.","title":"Quoc Le and Tomas Mikolov. Distributed representations of sentences and documents","authors":"Mikolov Le","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Levandowsky and Winter, 1971] Michael Levandowsky and David Winter. Distance between sets. Nature, 234(5323):34, 1971.","title":"Distance between sets","authors":"Michael Levandowsky, David Winter","year":"1971","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2016] Li Liu, William K Cheung, Xin Li, and Lejian Liao. Aligning users across social networks using network embedding. In IJCAI, pages 1774-1780, 2016.","title":"Aligning users across social networks using network embedding","authors":"Li Liu, William K Cheung, Xin Li, Lejian Liao","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/254"},{"position":14,"raw":"[Mikolov et al., 2013a] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. 2013.","title":"Efficient estimation of word representations in vector space","authors":"Mikolov","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mikolov et al., 2013b] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed repre- sentations of words and phrases and their compositionality. In NIPS, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Nallapati et al., 2008] Ramesh M Nallapati, Amr Ahmed, Eric P Xing, and William W Cohen. Joint latent topic models for text and citations. In KDD, pages 542-550. ACM, 2008.","title":"Joint latent topic models for text and citations","authors":"Ramesh M Nallapati, Amr Ahmed, Eric P Xing, William W Cohen","year":"2008","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pearson, 1901] Karl Pearson. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Sci- ence, 2(11):559-572, 1901.","title":"On lines and planes of closest fit to systems of points in space","authors":"Karl Pearson, Pearson","year":"1901","venue":"The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. DeepWalk: Online learning of social rep- resentations. In KDD, pages 701-710. ACM, 2014.","title":"DeepWalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ren et al., 2008] Wei Ren, Guiying Yan, Guohui Lin, Caifeng Du, and Xiaofeng Han. Detecting community structure by network vectorization. Computing and Com- binatorics, pages 245-254, 2008.","title":"Detecting community structure by network vectorization","authors":"Guiying Wei Ren, Guohui Yan, Caifeng Lin, Xiaofeng Du, Han","year":"2008","venue":"Computing and Combinatorics","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Rossi et al., 2013] Ryan A Rossi, Brian Gallagher, Jennifer Neville, and Keith Henderson. Modeling dynamic behav- ior in large evolving graphs. In WSDM, pages 667-676. ACM, 2013.","title":"Modeling dynamic behavior in large evolving graphs","authors":"Ryan A Rossi, Brian Gallagher, Jennifer Neville, Keith Henderson","year":"2013","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sun et al., 2016] Maosong Sun, Xinxiong Chen, Kaixu Zhang, Zhipeng Guo, and Zhiyuan Liu. THULAC: An efficient lexical analyzer for Chinese. 2016.","title":"THULAC: An efficient lexical analyzer for Chinese","authors":"Sun","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. LINE: Large- scale information network embedding. In WWW, pages 1067-1077. ACM, 2015.","title":"LINE: Largescale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tu et al., 2016] Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, and Maosong Sun. Max-margin DeepWalk: Discrim- inative learning of network representation. In IJCAI, pages 3889-3895, 2016.","title":"Max-margin DeepWalk: Discriminative learning of network representation","authors":"Tu","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/547"},{"position":24,"raw":"[Wilson et al., 2016] Kevin H Wilson, Yan Karklin, Bojian Han, and Chaitanya Ekanadham. Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation. In EDM, 2016.","title":"Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation","authors":"Yan Kevin H Wilson, Bojian Karklin, Chaitanya Han, Ekanadham","year":"2016","venue":"EDM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yang et al., 2016] Yi Yang, Ming-Wei Chang, and Jacob Eisenstein. Toward socially-infused information extrac- tion: Embedding authors, mentions, and entities. EMNLP, 2016.","title":"Toward socially-infused information extraction: Embedding authors, mentions, and entities","authors":"Yi Yang, Ming-Wei Chang, Jacob Eisenstein","year":"2016","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Yang, 1999] Yiming Yang. An evaluation of statistical ap- proaches to text categorization. Information retrieval, 1(1- 2):69-90, 1999.","title":"An evaluation of statistical approaches to text categorization","authors":"Yiming Yang","year":"1999","venue":"Information retrieval","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yu et al., 2015] Licheng Yu, Eunbyung Park, Alexander C Berg, and Tamara L Berg. Visual Madlibs: Fill in the blank description generation and question answering. In ICCV, pages 2461-2469, 2015.","title":"Visual Madlibs: Fill in the blank description generation and question answering","authors":"Licheng Yu, Eunbyung Park, Alexander C Berg, Tamara L Berg","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2017] Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. User profile preserving social net- work embedding. IJCAI, 2017.","title":"User profile preserving social network embedding","authors":"Zhang","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/472"},{"position":29,"raw":"[Zhao et al., 2010] Xiaohan Zhao, Alessandra Sala, Christo Wilson, Haitao Zheng, and Ben Y Zhao. Orion: short- est path estimation for large social graphs. Networks, 1:5, 2010.","title":"Orion: shortest path estimation for large social graphs","authors":"Xiaohan Zhao, Alessandra Sala, Christo Wilson, Haitao Zheng, Ben Y Zhao","year":"2010","venue":"Networks","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhao et al., 2011] Xiaohan Zhao, Alessandra Sala, Haitao Zheng, and Ben Y Zhao. Efficient shortest paths on mas- sive social graphs. In CollaborateCom, pages 77-86. IEEE, 2011.","title":"Efficient shortest paths on massive social graphs","authors":"Xiaohan Zhao, Alessandra Sala, Haitao Zheng, Ben Y Zhao","year":"2011","venue":"CollaborateCom","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}