{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:45Z","timestamp":1723016085490},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/455","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"3259-3265","source":"Crossref","is-referenced-by-count":1,"title":["Life-Stage Modeling by Customer-Manifold Embedding"],"prefix":"10.24963","author":[{"given":"Jing-Wen","family":"Yang","sequence":"first","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:53:59Z","timestamp":1501228439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/455"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/455","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}