{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-recurrent-neural-network-survival-model","title":"A Recurrent Neural Network Survival Model: Predicting Web User Return Time","arxiv_id":"1807.04098","date":"2018-07-11","proceeding":null,"authors":["Georg L. Grob","Ângelo Cardoso","C. H. Bryan Liu","Duncan A. Little","Benjamin Paul Chamberlain"],"abstract":"The size of a website's active user base directly affects its value. Thus, it\nis important to monitor and influence a user's likelihood to return to a site.\nEssential to this is predicting when a user will return. Current state of the\nart approaches to solve this problem come in two flavors: (1) Recurrent Neural\nNetwork (RNN) based solutions and (2) survival analysis methods. We observe\nthat both techniques are severely limited when applied to this problem.\nSurvival models can only incorporate aggregate representations of users instead\nof automatically learning a representation directly from a raw time series of\nuser actions. RNNs can automatically learn features, but can not be directly\ntrained with examples of non-returning users who have no target value for their\nreturn time. We develop a novel RNN survival model that removes the limitations\nof the state of the art methods. We demonstrate that this model can\nsuccessfully be applied to return time prediction on a large e-commerce dataset\nwith a superior ability to discriminate between returning and non-returning\nusers than either method applied in isolation.","url_abs":"http://arxiv.org/abs/1807.04098v1","url_pdf":"http://arxiv.org/pdf/1807.04098v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-recurrent-neural-network-survival-model","repo_url":"https://github.com/grobgl/rnnsm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"survival-analysis","task_name":"Survival Analysis"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}