%0 Conference Paper %1 Jing:2017:NSR:3018661.3018719 %A Jing, How %A Smola, Alexander J. %B Proceedings of the Tenth ACM International Conference on Web Search and Data Mining %C New York, NY, USA %D 2017 %I ACM %K just-in-time-recommendation recommendation-system social-information-access %P 515--524 %R 10.1145/3018661.3018719 %T Neural Survival Recommender %U http://doi.acm.org/10.1145/3018661.3018719 %X The ability to predict future user activity is invaluable when it comes to content recommendation and personalization. For instance, knowing when users will return to an online music service and what they will listen to increases user satisfaction and therefore user retention. We present a model based on Long-Short Term Memory to estimate when a user will return to a site and what their future listening behavior will be. In doing so, we aim to solve the problem of Just-In-Time recommendation, that is, to recommend the right items at the right time. We use tools from survival analysis for return time prediction and exponential families for future activity analysis. We show that the resulting multitask problem can be solved accurately, when applied to two real-world datasets. %@ 978-1-4503-4675-7

@inproceedings{Jing:2017:NSR:3018661.3018719, abstract = {The ability to predict future user activity is invaluable when it comes to content recommendation and personalization. For instance, knowing when users will return to an online music service and what they will listen to increases user satisfaction and therefore user retention. We present a model based on Long-Short Term Memory to estimate when a user will return to a site and what their future listening behavior will be. In doing so, we aim to solve the problem of Just-In-Time recommendation, that is, to recommend the right items at the right time. We use tools from survival analysis for return time prediction and exponential families for future activity analysis. We show that the resulting multitask problem can be solved accurately, when applied to two real-world datasets.}, acmid = {3018719}, added-at = {2017-02-08T21:44:53.000+0100}, address = {New York, NY, USA}, author = {Jing, How and Smola, Alexander J.}, biburl = {https://www.bibsonomy.org/bibtex/2a649896a236bf7345cfaa81a175dd596/xianteng}, booktitle = {Proceedings of the Tenth ACM International Conference on Web Search and Data Mining}, description = {Neural Survival Recommender}, doi = {10.1145/3018661.3018719}, interhash = {f1c4a2b174d26934cabfca7b71008678}, intrahash = {a649896a236bf7345cfaa81a175dd596}, isbn = {978-1-4503-4675-7}, keywords = {just-in-time-recommendation recommendation-system social-information-access}, location = {Cambridge, United Kingdom}, numpages = {10}, pages = {515--524}, publisher = {ACM}, series = {WSDM '17}, timestamp = {2017-02-08T21:44:53.000+0100}, title = {Neural Survival Recommender}, url = {http://doi.acm.org/10.1145/3018661.3018719}, year = 2017 }