{"type":"ScholarlyArticle","title":"Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes","authors":[{"name":"Hongteng Xu","slug":"hongteng-xu","given":"Hongteng","family":"Xu","affiliation":"Department of Electrical and Computer Engineering , Duke University"},{"name":"Dixin Luo","slug":"dixin-luo","given":"Dixin","family":"Luo","affiliation":"Department of Electrical and Computer Engineering , Duke University"},{"name":"Lawrence Carin","slug":"lawrence-carin","given":"Lawrence","family":"Carin","affiliation":"Department of Electrical and Computer Engineering , Duke University"}],"abstract":"A continuous-time tensor factorization method is developed for event sequences containing multiple \"modalities.\" Each data element is a point in a tensor, whose dimensions are associated with  the discrete alphabet of the modalities. Each tensor data element has an associated time of occurence and a feature vector. We model such data based on pairwise interactive point processes, and the proposed framework connects pairwise tensor factorization with a feature-embedded point process. The model accounts for interactions within each modality, interactions across different modalities, and continuous-time dynamics of the interactions. Model learning is formulated as a convex optimization problem, based on online alternating direction method of multipliers. Compared to existing state-of-the-art methods, our approach captures the latent structure of the tensor and its evolution over time, obtaining superior results on real-world datasets.","doi":"10.24963/ijcai.2018/403","url":"https://ijcai.org/proceedings/2018/403","pdf":"https://ijcai.org/proceedings/2018/0403.pdf","markdown":"https://ijcai.org/proceedings/2018/403.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/403","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":"Machine Learning","track":"Main track","number":403,"pages":{"first":2905,"last":2911},"keywords":["Machine Learning: Time-series;Data Streams","Machine Learning: Machine Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Acharya et al., 2015] Ayan Acharya, Joydeep Ghosh, and Mingyuan Zhou. Nonparametric Bayesian factor analysis for dynamic count matrices. arXiv preprint arXiv:1512.08996, 2015.","title":"Nonparametric Bayesian factor analysis for dynamic count matrices","authors":"Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bacry et al., 2015] Emmanuel Bacry, Iacopo Mastromatteo, and Jean-Franc ¸ois Muzy. Hawkes processes in finance. Market Mi- crostructure and Liquidity, 1(01):1550005, 2015.","title":"Hawkes processes in finance","authors":"Emmanuel Bacry, Iacopo Mastromatteo, Jean-Franc ¸ois Muzy","year":"2015","venue":"Market Microstructure and Liquidity","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Blundell et al., 2012] Charles Blundell, Jeff Beck, and Kather- ine A Heller. Modelling reciprocating relationships with Hawkes processes. In NIPS, 2012.","title":"Modelling reciprocating relationships with Hawkes processes","authors":"Charles Blundell, Jeff Beck, Katherine A Heller","year":"2012","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Charlin et al., 2015] Laurent Charlin, Rajesh Ranganath, James McInerney, and David M Blei. Dynamic Poisson factorization. In RecSys, 2015.","title":"Dynamic Poisson factorization","authors":"Laurent Charlin, Rajesh Ranganath, James Mcinerney, David M Blei","year":"2015","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Du et al., 2015] Nan Du, Yichen Wang, Niao He, Jimeng Sun, and Le Song. Time-sensitive recommendation from recurrent user activities. In NIPS, 2015.","title":"Time-sensitive recommendation from recurrent user activities","authors":"Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dunlavy et al., 2011] Daniel M Dunlavy, Tamara G Kolda, and Evrim Acar. Temporal link prediction using matrix and tensor factorizations. Transactions on Knowledge Discovery from Data, 5(2):10, 2011.","title":"Temporal link prediction using matrix and tensor factorizations","authors":"Tamara G Daniel M Dunlavy, Evrim Kolda, Acar","year":"2011","venue":"Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hall and Willett, 2016] Eric C Hall and Rebecca M Willett. Track- ing dynamic point processes on networks. Transactions on Infor- mation Theory, 62(7):4327-4346, 2016.","title":"Tracking dynamic point processes on networks","authors":"C Eric, Rebecca M Hall, Willett","year":"2016","venue":"Transactions on Information Theory","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hawkes, 1971] Alan G Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83-90, 1971.","title":"Spectra of some self-exciting and mutually exciting point processes","authors":"Alan G Hawkes","year":"1971","venue":"Biometrika","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hu et al., 2015] Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, and Lawrence Carin. Scalable Bayesian non- negative tensor factorization for massive count data. In ECML, 2015.","title":"Scalable Bayesian nonnegative tensor factorization for massive count data","authors":"Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin","year":"2015","venue":"ECML","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kolda and Bader, 2009] Tamara G Kolda and Brett W Bader. Ten- sor decompositions and applications. SIAM review, 51(3):455- 500, 2009.","title":"Tensor decompositions and applications","authors":"G Tamara, Brett W Kolda, Bader","year":"2009","venue":"SIAM review","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Koren et al., 2009] Yehuda Koren, Robert Bell, and Chris Volin- sky. Matrix factorization techniques for recommender systems. Computer, 42(8), 2009.","title":"Matrix factorization techniques for recommender systems","authors":"Koren","year":"2009","venue":"Computer","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Koren, 2010] Yehuda Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 53(4):89-97, 2010.","title":"Collaborative filtering with temporal dynamics","authors":"Koren ;, Yehuda Koren","year":"2010","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kuang et al., 2012] Da Kuang, Chris Ding, and Haesun Park. Symmetric nonnegative matrix factorization for graph clustering. In SDM, 2012.","title":"Symmetric nonnegative matrix factorization for graph clustering","authors":"Da Kuang, Chris Ding, Haesun Park","year":"2012","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Leetaru and Schrodt, 2013] Kalev Leetaru and Philip A Schrodt. GDELT: Global data on events, location, and tone. In ISA Annual Convention, 2013.","title":"GDELT: Global data on events, location, and tone","authors":"Schrodt Leetaru, Kalev Leetaru, Philip A Schrodt","year":"2013","venue":"ISA Annual Convention","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lewis and Mohler, 2011] Erik Lewis and George Mohler. A non- parametric EM algorithm for multiscale Hawkes processes. Jour- nal of Nonparametric Statistics, 1(1):1-20, 2011.","title":"A nonparametric EM algorithm for multiscale Hawkes processes","authors":"Lewis, Mohler, Erik Lewis, George Mohler","year":"2011","venue":"Journal of Nonparametric Statistics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Luo et al., 2014] Dixin Luo, Hongteng Xu, Hongyuan Zha, Jun Du, Rong Xie, Xiaokang Yang, and Wenjun Zhang. You are what you watch and when you watch: Inferring household struc- tures from IPTV viewing data. Transactions on Broadcasting, 60(1):61-72, 2014.","title":"You are what you watch and when you watch: Inferring household structures from IPTV viewing data","authors":"Luo","year":"2014","venue":"Transactions on Broadcasting","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Luo et al., 2015] Dixin Luo, Hongteng Xu, Yi Zhen, Xia Ning, Hongyuan Zha, Xiaokang Yang, and Wenjun Zhang. Multi- task multi-dimensional Hawkes processes for modeling event se- quences. In IJCAI, 2015.","title":"Multitask multi-dimensional Hawkes processes for modeling event sequences","authors":"Luo","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ogata, 1981] Yosihiko Ogata. On Lewis' simulation method for point processes. Transactions on Information Theory, 27(1):23- 31, 1981.","title":"Yosihiko Ogata. On Lewis' simulation method for point processes","authors":"Ogata","year":"1981","venue":"Transactions on Information Theory","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ouyang et al., 2013] Hua Ouyang, Niao He, Long Tran, and Alexander Gray. Stochastic alternating direction method of mul- tipliers. In ICML, 2013.","title":"Stochastic alternating direction method of multipliers","authors":"Hua Ouyang, Niao He, Long Tran, Alexander Gray","year":"2013","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ozerov et al., 2011] Alexey Ozerov, Cédric Févotte, Raphaël Blouet, and Jean-Louis Durrieu. Multichannel nonnegative ten- sor factorization with structured constraints for user-guided audio source separation. In ICASSP, 2011.","title":"Multichannel nonnegative tensor factorization with structured constraints for user-guided audio source separation","authors":"Alexey Ozerov, Cédric Févotte, Raphaël Blouet, Jean-Louis Durrieu","year":"2011","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Park and Chu, 2009] Seung-Taek Park and Wei Chu. Pairwise preference regression for cold-start recommendation. In RecSys, 2009.","title":"Pairwise preference regression for cold-start recommendation","authors":"Seung-Taek Park, Wei Chu","year":"2009","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Rafailidis and Nanopoulos, 2014] Dimitrios Rafailidis and Alexandros Nanopoulos. Modeling the dynamics of user preferences in coupled tensor factorization. In RecSys, 2014.","title":"Modeling the dynamics of user preferences in coupled tensor factorization","authors":"Dimitrios Rafailidis, Alexandros Nanopoulos","year":"2014","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Rai et al., 2015] Piyush Rai, Yingjian Wang, and Lawrence Carin. Leveraging features and networks for probabilistic tensor decom- position. In AAAI, 2015.","title":"Leveraging features and networks for probabilistic tensor decomposition","authors":"Piyush Rai, Yingjian Wang, Lawrence Carin","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Rendle and Schmidt-Thieme, 2010] Steffen Rendle and Lars Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In WSDM, 2010.","title":"Pairwise interaction tensor factorization for personalized tag recommendation","authors":"Steffen Rendle, Lars Schmidt-Thieme","year":"2010","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Shamir, 2015] Ohad Shamir. The sample complexity of learn- ing linear predictors with the squared loss. Journal of Machine Learning Research, 16:3475-3486, 2015.","title":"The sample complexity of learning linear predictors with the squared loss","authors":"Ohad Shamir","year":"2015","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shashua and Hazan, 2005] Amnon Shashua and Tamir Hazan. Non-negative tensor factorization with applications to statistics and computer vision. In ICML, 2005.","title":"Non-negative tensor factorization with applications to statistics and computer vision","authors":"Hazan Shashua, Amnon Shashua, Tamir Hazan","year":"2005","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Tucker, 1966] Ledyard R Tucker. Some mathematical notes on three-mode factor analysis. Psychometrika, 31(3):279-311, 1966.","title":"Some mathematical notes on three-mode factor analysis","authors":"R Ledyard, Tucker","year":"1966","venue":"Psychometrika","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2016] Yichen Wang, Nan Du, Rakshit Trivedi, and Le Song. Coevolutionary latent feature processes for continuous- time user-item interactions. In NIPS, 2016.","title":"Coevolutionary latent feature processes for continuoustime user-item interactions","authors":"Yichen Wang, Nan Du, Rakshit Trivedi, Le Song","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Xiong et al., 2010] Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, and Jaime G Carbonell. Temporal collaborative fil- tering with Bayesian probabilistic tensor factorization. In SDM, 2010.","title":"Temporal collaborative filtering with Bayesian probabilistic tensor factorization","authors":"Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G Carbonell","year":"2010","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Xu and Zha, 2017] Hongteng Xu and Hongyuan Zha. A Dirichlet mixture model of Hawkes processes for event sequence cluster- ing. In NIPS, 2017.","title":"A Dirichlet mixture model of Hawkes processes for event sequence clustering","authors":"Zha Xu, Hongteng Xu, Hongyuan Zha","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Xu et al., 2015] Hongteng Xu, Yi Zhen, and Hongyuan Zha. Trailer generation via a point process-based visual attractiveness model. In IJCAI, 2015.","title":"Trailer generation via a point process-based visual attractiveness model","authors":"Xu","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/311"},{"position":32,"raw":"[Xu et al., 2017a] Hongteng Xu, Dixin Luo, and Hongyuan Zha. Learning Hawkes processes from short doubly-censored event se- quences. In ICML, 2017.","title":"Learning Hawkes processes from short doubly-censored event sequences","authors":"Xu","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xu et al., 2017b] Hongteng Xu, Weichang Wu, Shamim Nemati, and Hongyuan Zha. Patient flow prediction via discrimina- tive learning of mutually-correcting processes. Transactions on Knowledge and Data Engineering, 29(1):157-171, 2017.","title":"Patient flow prediction via discriminative learning of mutually-correcting processes","authors":"Xu","year":"2017","venue":"Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Yang et al., 2017] Yingxiang Yang, Jalal Etesami, Niao He, and Negar Kiyavash. Online learning for multivariate Hawkes pro- cesses. In NIPS, 2017.","title":"Online learning for multivariate Hawkes processes","authors":"Yang","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zass and Shashua, 2007] Ron Zass and Amnon Shashua. Nonneg- ative sparse PCA. In NIPS, 2007.","title":"Nonnegative sparse PCA","authors":"Ron Zass, Amnon Shashua","year":"2007","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhong and Kwok, 2014] Wenliang Zhong and James Kwok. Fast stochastic alternating direction method of multipliers. In ICML, 2014.","title":"Fast stochastic alternating direction method of multipliers","authors":"Wenliang Zhong, James Kwok","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}