%0 Conference Paper %1 tavakol2014factored %A Tavakol, Maryam %A Brefeld, Ulf %B Proceedings of the 8th ACM Conference on Recommender Systems %C New York, NY, USA %D 2014 %I ACM %K knn %P 33--40 %R 10.1145/2645710.2645739 %T Factored MDPs for Detecting Topics of User Sessions %U http://doi.acm.org/10.1145/2645710.2645739 %X Recommender systems aim to capture interests of users to provide tailored recommendations. User interests are however often unique and depend on many unobservable factors including a user's mood and the local weather. We take a contextual session-based approach and propose a sequential framework using factored Markov decision processes (fMDPs) to detect the user's goal (the topic) of a session. We show that an independence assumption on the attributes of items leads to a set of independent models that can be optimised efficiently. Our approach results in interpretable topics that can be effectively turned into recommendations. Empirical results on a real world click log from a large e-commerce company exhibit highly accurate topic prediction rates of about 90%. Translating our approach into a topic-driven recommender system outperforms several baseline competitors. %@ 978-1-4503-2668-1

@inproceedings{tavakol2014factored, abstract = {Recommender systems aim to capture interests of users to provide tailored recommendations. User interests are however often unique and depend on many unobservable factors including a user's mood and the local weather. We take a contextual session-based approach and propose a sequential framework using factored Markov decision processes (fMDPs) to detect the user's goal (the topic) of a session. We show that an independence assumption on the attributes of items leads to a set of independent models that can be optimised efficiently. Our approach results in interpretable topics that can be effectively turned into recommendations. Empirical results on a real world click log from a large e-commerce company exhibit highly accurate topic prediction rates of about 90%. Translating our approach into a topic-driven recommender system outperforms several baseline competitors.}, acmid = {2645739}, added-at = {2018-11-26T13:08:32.000+0100}, address = {New York, NY, USA}, author = {Tavakol, Maryam and Brefeld, Ulf}, biburl = {https://www.bibsonomy.org/bibtex/2d4e510f7cc1c93b1fbc6f6ad52178c08/theresa_rudolph}, booktitle = {Proceedings of the 8th ACM Conference on Recommender Systems}, description = {Factored MDPs for detecting topics of user sessions}, doi = {10.1145/2645710.2645739}, interhash = {4349cb8328e893f388785c407e70dc97}, intrahash = {d4e510f7cc1c93b1fbc6f6ad52178c08}, isbn = {978-1-4503-2668-1}, keywords = {knn}, location = {Foster City, Silicon Valley, California, USA}, numpages = {8}, pages = {33--40}, publisher = {ACM}, series = {RecSys '14}, timestamp = {2018-11-26T13:08:44.000+0100}, title = {Factored MDPs for Detecting Topics of User Sessions}, url = {http://doi.acm.org/10.1145/2645710.2645739}, year = 2014 }