{"type":"ScholarlyArticle","title":"Life-Stage Modeling by Customer-Manifold Embedding","authors":[{"name":"Jing-Wen Yang","slug":"jing-wen-yang","given":"Jing-Wen","family":"Yang","affiliation":"† National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China; † Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing , China"},{"name":"Yang Yu","slug":"yang-yu","given":"Yang","family":"Yu","affiliation":"† National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China; † Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing , China"},{"name":"Xiao-Peng Zhang","slug":"xiao-peng-zhang","given":"Xiao-Peng","family":"Zhang","affiliation":"‡ Tencent Inc. , China"}],"abstract":"A person experiences different stages throughout the life, causing dramatically varying behavior patterns. In applications such as online-shopping, it has been observed that customer behaviors are largely affected by their stages and are evolving over time. Although this phenomena has been recognized previously, very few studies tried to model the life-stage and make use of it. In this paper, we propose to discover a latent space, called customer-manifold, on which a position corresponds to a customer stage. The customer-manifold allows us to train a static prediction model that captures dynamic customer behavior patterns. We further embed the learned customer-manifold into a neural network model as a hidden layer output, resulting in an efficient and accurate customer behavior prediction system. We apply this system to online-shopping recommendation. Experiments in real world data show that taking customer-manifold into account can improve the performance of the recommender system. Moreover, visualization of the customer-manifold space may also be helpful to understand the evolutionary customer behaviors.","doi":"10.24963/ijcai.2017/455","url":"https://ijcai.org/proceedings/2017/455","pdf":"https://ijcai.org/proceedings/2017/0455.pdf","markdown":"https://ijcai.org/proceedings/2017/455.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/455","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 S-Z","track":"Main track","number":455,"pages":{"first":3259,"last":3265},"keywords":["Multidisciplinary Topics and Applications: Personalization and User Modeling","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Berndt and Clifford, 1994] Donald J. Berndt and James. Clifford. Using dynamic time warping to find patterns in time series. In Proceedings of the 1th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 359-370, 1994.","title":"Using dynamic time warping to find patterns in time series","authors":"Donald J Berndt, James Clifford","year":"1994","venue":"Proceedings of the 1th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bojanic, 2011] David C. Bojanic. The impact of age and family life experiences on mexican visitor shopping expen- ditures. Tourism Management, 32(2):406-414, 2011.","title":"The impact of age and family life experiences on mexican visitor shopping expenditures","authors":"C David, Bojanic","year":"2011","venue":"Tourism Management","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chu et al., 2002] Selina Chu, Eamonn J. Keogh, David M. Hart, and Michael J. Pazzani. Iterative deepening dynamic time warping for time series. In Proceedings of the 2nd SIAM International Conference on Data Mining, pages 195-212, 2002.","title":"Iterative deepening dynamic time warping for time series","authors":"Selina Chu, Eamonn J Keogh, David M Hart, Michael J Pazzani","year":"2002","venue":"Proceedings of the 2nd SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ding and Li, 2005] Yi Ding and Xue Li. Time weight col- laborative filtering. In Proceedings of the 14th ACM Inter- national Conference on Information and Knowledge Man- agement, pages 485-492, 2005.","title":"Time weight collaborative filtering","authors":"Yi Ding, Xue Li","year":"2005","venue":"Proceedings of the 14th ACM International Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ferwerda and Schedl, 2016] Bruce Ferwerda and Markus Schedl. Personality-based user modeling for music rec- ommender systems. In Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part III, pages 254-257, 2016.","title":"Personality-based user modeling for music recommender systems","authors":"Schedl Ferwerda, Bruce Ferwerda, Markus Schedl","year":"2016","venue":"Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2016","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hidasi et al., 2015] Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based rec- ommendations with recurrent neural networks. CoRR, abs/1511.06939, 2015.","title":"Session-based recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2008] Yifan Hu, Yehuda Koren, and Chris Volin- sky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE International Conference on Data Mining, pages 263-272, 2008.","title":"Collaborative filtering for implicit feedback datasets","authors":"Yifan Hu, Yehuda Koren, Chris Volinsky","year":"2008","venue":"Proceedings of the 8th IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jiang et al., 2015] Peng Jiang, Yadong Zhu, Yi Zhang, and Quan Yuan. Life-stage prediction for product recommen- dation in e-commerce. In Proceedings of the 21th Interna- tional ACM SIGKDD Conference on Knowledge discovery and data mining, pages 1879-1888, 2015.","title":"Life-stage prediction for product recommendation in e-commerce","authors":"Peng Jiang, Yadong Zhu, Yi Zhang, Quan Yuan","year":"2015","venue":"Proceedings of the 21th International ACM SIGKDD Conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Koren, 2008] Yehuda Koren. Factorization meets the neigh- borhood: A multifaceted collaborative filtering model. In Proceedings of the 14th International ACM SIGKDD Con- ference on Knowledge Discovery and Data Mining, pages 426-434, 2008.","title":"Factorization meets the neighborhood: A multifaceted collaborative filtering model","authors":"Koren ;, Yehuda Koren","year":"2008","venue":"Proceedings of the 14th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":10,"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":11,"raw":"[Lathia et al., 2009] Neal Lathia, Stephen Hailes, and Li- cia Capra. Temporal collaborative filtering with adaptive neighbourhoods. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 796-797, 2009.","title":"Temporal collaborative filtering with adaptive neighbourhoods","authors":"Neal Lathia, Stephen Hailes, Licia Capra","year":"2009","venue":"Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Le et al., 2016] Duc-Trong Le, Yuan Fang, and Hady Wirawan Lauw. Modeling sequential prefer- ences with dynamic user and context factors. In Machine Learning and Knowledge Discovery in Databases -Eu- ropean Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II, pages 145-161, 2016.","title":"Modeling sequential preferences with dynamic user and context factors","authors":"Duc-Trong Le, Yuan Fang, Hady Wirawan Lauw","year":"2016","venue":"Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2016","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2011] Ruijiang Li, Bin Li, Cheng Jin, Xiangyang Xue, and Xingquan Zhu. Tracking user-preference vary- ing speed in collaborative filtering. In Proceedings of the 25th AAAI Conference on Artificial Intelligence, pages 133-138, 2011.","title":"Tracking user-preference varying speed in collaborative filtering","authors":"Li","year":"2011","venue":"Proceedings of the 25th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2010] Nathan Nan Liu, Min Zhao, Evan Wei Xi- ang, and Qiang Yang. Online evolutionary collaborative filtering. In Proceedings of the 4th ACM Conference on Recommender Systems, pages 95-102, 2010.","title":"Online evolutionary collaborative filtering","authors":"Nan Nathan, Min Liu, Evan Wei Zhao, Qiang Xiang, Yang","year":"2010","venue":"Proceedings of the 4th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2014] Chuanren Liu, Kai Zhang, Hui Xiong, Ge- off Jiang, and Qiang Yang. Temporal skeletonization on sequential data: Patterns, categorization, and visualization. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1336-1345, 2014.","title":"Temporal skeletonization on sequential data: Patterns, categorization, and visualization","authors":"Chuanren Liu, Kai Zhang, Hui Xiong, Geoff Jiang, Qiang Yang","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu, 2015] Xin Liu. Modeling users' dynamic preference for personalized recommendation. In Proceedings of the 24th International Joint Conference on Artificial Intelli- gence, pages 1785-1791, 2015.","title":"Modeling users' dynamic preference for personalized recommendation","authors":"Xin Liu","year":"2015","venue":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/254"},{"position":17,"raw":"[Paterek, 2007] Arkadiusz Paterek. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop, 2007.","title":"Improving regularized singular value decomposition for collaborative filtering","authors":"Arkadiusz Paterek","year":"2007","venue":"Proceedings of KDD Cup and Workshop","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Rendle and Freudenthaler, 2014] Steffen Rendle and Christoph Freudenthaler. Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pages 273-282, 2014.","title":"Improving pairwise learning for item recommendation from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler","year":"2014","venue":"Proceedings of the 7th ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Resnick et al., 1994] Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. Grou- pLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pages 175-186, 1994.","title":"Grou-pLens: An open architecture for collaborative filtering of netnews","authors":"Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, John Riedl","year":"1994","venue":"Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Sakoe and Chiba, 1978] Hiroaki Sakoe and Seibi Chiba. Dynamic programming algorithm optimization for spoken word recognition. In IEEE Transactions on Acoustics, Speech and Signal Processing, pages 43-49, 1978.","title":"Dynamic programming algorithm optimization for spoken word recognition","authors":"Hiroaki Sakoe, Seibi Chiba","year":"1978","venue":"IEEE Transactions on Acoustics, Speech and Signal Processing","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 Proceedings of the 10th International Conference on World Wide Web, pages 285-295, 2001.","title":"Item-based collaborative filtering recommendation algorithms","authors":"Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl","year":"2001","venue":"Proceedings of the 10th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tenenbaum et al., 2000] Joshua B. Tenenbaum, Vin De Silva, and John C. Langford. A global geometric frame- work for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.","title":"A global geometric framework for nonlinear dimensionality reduction","authors":"Joshua B Tenenbaum, Vin De Silva, John C Langford","year":"2000","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wells and Gubar, 1966] William D. Wells and George Gubar. Life cycle concept in marketing research. Journal of Marketing Research, 3(4):355-363, 1966.","title":"Life cycle concept in marketing research","authors":"William D Wells, George Gubar","year":"1966","venue":"Journal of Marketing Research","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xiong et al., 2010] Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff G. Schneider, and Jaime G. Carbonell. Temporal col- laborative filtering with bayesian probabilistic tensor fac- torization. In Proceedings of the 10th SIAM International Conference on Data Mining, pages 211-222, 2010.","title":"Temporal collaborative filtering with bayesian probabilistic tensor factorization","authors":"Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff G Schneider, Jaime G Carbonell","year":"2010","venue":"Proceedings of the 10th SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}