%0 Conference Paper %1 Wu:2017:RRN:3018661.3018689 %A Wu, Chao-Yuan %A Ahmed, Amr %A Beutel, Alex %A Smola, Alexander J. %A Jing, How %B Proceedings of the Tenth ACM International Conference on Web Search and Data Mining %C New York, NY, USA %D 2017 %I ACM %K recommendation-system social-information-access temporal-dynamics %P 495--503 %R 10.1145/3018661.3018689 %T Recurrent Recommender Networks %U http://doi.acm.org/10.1145/3018661.3018689 %X Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function. %@ 978-1-4503-4675-7

@inproceedings{Wu:2017:RRN:3018661.3018689, abstract = {Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.}, acmid = {3018689}, added-at = {2017-02-08T21:39:59.000+0100}, address = {New York, NY, USA}, author = {Wu, Chao-Yuan and Ahmed, Amr and Beutel, Alex and Smola, Alexander J. and Jing, How}, biburl = {https://www.bibsonomy.org/bibtex/2fd4e74238bf7d7b5973c05f74a7adc89/xianteng}, booktitle = {Proceedings of the Tenth ACM International Conference on Web Search and Data Mining}, description = {Recurrent Recommender Networks}, doi = {10.1145/3018661.3018689}, interhash = {6c115d03d5066bcb8a201ed19e8fab19}, intrahash = {fd4e74238bf7d7b5973c05f74a7adc89}, isbn = {978-1-4503-4675-7}, keywords = {recommendation-system social-information-access temporal-dynamics}, location = {Cambridge, United Kingdom}, numpages = {9}, pages = {495--503}, publisher = {ACM}, series = {WSDM '17}, timestamp = {2017-02-08T21:39:59.000+0100}, title = {Recurrent Recommender Networks}, url = {http://doi.acm.org/10.1145/3018661.3018689}, year = 2017 }