Difference between revisions of "2017 WhenRecurrentNeuralNetworksMeet"

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m (Text replacement - " Yizhou Sun" to " Yizhou Sun")
m (Text replacement - " Paul Covington" to " Paul Covington")
 
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* 3. Shuo Chen, Josh L. Moore, Douglas Turnbull, Thorsten Joachims, Playlist Prediction via Metric Embedding, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 12-16, 2012, Beijing, China [http://doi.acm.org/10.1145/2339530.2339643 doi:10.1145/2339530.2339643]
 
* 3. Shuo Chen, Josh L. Moore, Douglas Turnbull, Thorsten Joachims, Playlist Prediction via Metric Embedding, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 12-16, 2012, Beijing, China [http://doi.acm.org/10.1145/2339530.2339643 doi:10.1145/2339530.2339643]
 
* 4. Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and [[Yoshua Bengio]]. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR Abs/1412.3555 ([[2014]]). http://arxiv.org/abs/1412.3555
 
* 4. Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and [[Yoshua Bengio]]. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR Abs/1412.3555 ([[2014]]). http://arxiv.org/abs/1412.3555
* 5. Paul Covington, Jay Adams, Emre Sargin, Deep Neural Networks for YouTube Recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA [http://doi.acm.org/10.1145/2959100.2959190 doi:10.1145/2959100.2959190]
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* 5. [[Paul Covington]], Jay Adams, Emre Sargin, Deep Neural Networks for YouTube Recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA [http://doi.acm.org/10.1145/2959100.2959190 doi:10.1145/2959100.2959190]
 
* 6. Sander Dieleman, Keynote: Deep Learning for Audio-based Music Recommendation, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, p.1-1, September 15-15, 2016, Boston, MA, USA [http://doi.acm.org/10.1145/2988450.2991128 doi:10.1145/2988450.2991128]
 
* 6. Sander Dieleman, Keynote: Deep Learning for Audio-based Music Recommendation, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, p.1-1, September 15-15, 2016, Boston, MA, USA [http://doi.acm.org/10.1145/2988450.2991128 doi:10.1145/2988450.2991128]
 
* 7. Ali Mamdouh Elkahky, Yang Song, Xiaodong He, A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems, Proceedings of the 24th International Conference on World Wide Web, May 18-22, 2015, Florence, Italy [http://doi.acm.org/10.1145/2736277.2741667 doi:10.1145/2736277.2741667]
 
* 7. Ali Mamdouh Elkahky, Yang Song, Xiaodong He, A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems, Proceedings of the 24th International Conference on World Wide Web, May 18-22, 2015, Florence, Italy [http://doi.acm.org/10.1145/2736277.2741667 doi:10.1145/2736277.2741667]

Latest revision as of 22:35, 26 March 2020

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Abstract

Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem of making session-based recommendations, i.e., for recommending the next item in an anonymous session, (Hidasi et al. ~) recently investigated the application of recurrent neural networks with Gated Recurrent Units (GRU4REC). Assessing the true effectiveness of such novel approaches based only on what is reported in the literature is however difficult when no standard evaluation protocols are applied and when the strength of the baselines used in the performance comparison is not clear. In this work we show based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets. Neighborhood sampling and efficient in-memory data structures ensure the scalability of the kNN method. The best results in the end were often achieved when we combine the kNN approach with GRU4REC, which shows that RNNs can leverage sequential signals in the data that cannot be detected by the co-occurrence-based kNN method.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2017 WhenRecurrentNeuralNetworksMeetDietmar Jannach
Malte Ludewig
When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation10.1145/3109859.31098722017
AuthorDietmar Jannach + and Malte Ludewig +
doi10.1145/3109859.3109872 +
titleWhen Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation +
year2017 +