%0 Generic %1 Figueiredo2015TribeFlow %A Figueiredo, Flavio %A Ribeiro, Bruno %A Almeida, Jussara %A Faloutsos, Christos %D 2015 %K preprint predictive-models human-behaviour marketing %T TribeFlow: Mining & Predicting User Trajectories %U http://arxiv.org/abs/1511.01032 %X Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges we propose TribeFlow, a method designed to cope with the complex challenges of learning personalized predictive models of non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow is a general method that can perform next product recommendation, next song recommendation, next location prediction, and general arbitrary-length user trajectory prediction without domain-specific knowledge. TribeFlow is more accurate and up to 413x faster than top competitors.

@misc{Figueiredo2015TribeFlow, abstract = {{Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges we propose TribeFlow, a method designed to cope with the complex challenges of learning personalized predictive models of non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow is a general method that can perform next product recommendation, next song recommendation, next location prediction, and general arbitrary-length user trajectory prediction without domain-specific knowledge. TribeFlow is more accurate and up to 413x faster than top competitors.}}, added-at = {2019-06-10T14:53:09.000+0200}, archiveprefix = {arXiv}, author = {Figueiredo, Flavio and Ribeiro, Bruno and Almeida, Jussara and Faloutsos, Christos}, biburl = {https://www.bibsonomy.org/bibtex/2da61cdcb8efde673f67c31611daf6379/nonancourt}, citeulike-article-id = {13827750}, citeulike-linkout-0 = {http://arxiv.org/abs/1511.01032}, citeulike-linkout-1 = {http://arxiv.org/pdf/1511.01032}, day = 3, eprint = {1511.01032}, interhash = {589d5e8effdd8a5441bcb52dc88c5bd2}, intrahash = {da61cdcb8efde673f67c31611daf6379}, keywords = {preprint predictive-models human-behaviour marketing}, month = nov, posted-at = {2015-11-04 13:44:03}, priority = {2}, timestamp = {2019-08-22T16:20:33.000+0200}, title = {{TribeFlow: Mining \& Predicting User Trajectories}}, url = {http://arxiv.org/abs/1511.01032}, year = 2015 }