%0 Conference Paper %1 tan2016improved %A Tan, Yong Kiam %A Xu, Xinxing %A Liu, Yong %B Proceedings of the 1st Workshop on Deep Learning for Recommender Systems %C New York, NY, USA %D 2016 %I ACM %K recommendation rnn session %P 17--22 %R 10.1145/2988450.2988452 %T Improved Recurrent Neural Networks for Session-based Recommendations %U http://doi.acm.org/10.1145/2988450.2988452 %X Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively. %@ 978-1-4503-4795-2

@inproceedings{tan2016improved, abstract = {Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.}, acmid = {2988452}, added-at = {2017-02-26T18:07:29.000+0100}, address = {New York, NY, USA}, author = {Tan, Yong Kiam and Xu, Xinxing and Liu, Yong}, biburl = {https://www.bibsonomy.org/bibtex/297a26821c78b1b68b26ca559bf9a6c2e/nosebrain}, booktitle = {Proceedings of the 1st Workshop on Deep Learning for Recommender Systems}, doi = {10.1145/2988450.2988452}, interhash = {7dd7bde2439a64bdf01bac1202d701fd}, intrahash = {97a26821c78b1b68b26ca559bf9a6c2e}, isbn = {978-1-4503-4795-2}, keywords = {recommendation rnn session}, location = {Boston, MA, USA}, numpages = {6}, pages = {17--22}, publisher = {ACM}, series = {DLRS 2016}, timestamp = {2017-02-26T18:07:29.000+0100}, title = {Improved Recurrent Neural Networks for Session-based Recommendations}, url = {http://doi.acm.org/10.1145/2988450.2988452}, year = 2016 }