Difference between revisions of "2019 DeepLearningBasedRecommenderSys"

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* 25. Wei-Ta Chu, Ya-Lun Tsai, A Hybrid Recommendation System Considering Visual Information for Predicting Favorite Restaurants, World Wide Web, v.20 n.6, p.1313-1331, November 2017 [https://dx.doi.org/10.1007/s11280-017-0437-1 doi:10.1007/s11280-017-0437-1]
 
* 25. Wei-Ta Chu, Ya-Lun Tsai, A Hybrid Recommendation System Considering Visual Information for Predicting Favorite Restaurants, World Wide Web, v.20 n.6, p.1313-1331, November 2017 [https://dx.doi.org/10.1007/s11280-017-0437-1 doi:10.1007/s11280-017-0437-1]
 
* 26. Ronan Collobert, [[Jason Weston]], A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Proceedings of the 25th International Conference on Machine Learning, p.160-167, July 05-09, 2008, Helsinki, Finland [http://doi.acm.org/10.1145/1390156.1390177 doi:10.1145/1390156.1390177]
 
* 26. Ronan Collobert, [[Jason Weston]], A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Proceedings of the 25th International Conference on Machine Learning, p.160-167, July 05-09, 2008, Helsinki, Finland [http://doi.acm.org/10.1145/1390156.1390177 doi:10.1145/1390156.1390177]
* 27. 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]
+
* 27. [[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]
 
* 28. Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Deep Coevolutionary Network: Embedding User and Item Features for Recommendation. ArXiv Preprint. ArXiv Preprint ArXiv:1609.03675 (2016).
 
* 28. Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Deep Coevolutionary Network: Embedding User and Item Features for Recommendation. ArXiv Preprint. ArXiv Preprint ArXiv:1609.03675 (2016).
 
* 29. Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song, Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, p.29-34, September 15-15, 2016, Boston, MA, USA [http://doi.acm.org/10.1145/2988450.2988451 doi:10.1145/2988450.2988451]
 
* 29. Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song, Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, p.29-34, September 15-15, 2016, Boston, MA, USA [http://doi.acm.org/10.1145/2988450.2988451 doi:10.1145/2988450.2988451]

Revision as of 22:37, 26 March 2020

Subject Headings: Deep Learning Based Recommener System, Deep Learning Neural Network.

Notes

Cited By

Quotes

Abstract

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.

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}};


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2019 DeepLearningBasedRecommenderSysShuai Zhang
Lina Yao
Aixin Sun
Yi Tay
Deep Learning Based Recommender System: A Survey and New Perspectives10.1145/32850292019
AuthorShuai Zhang +, Lina Yao +, Aixin Sun + and Yi Tay +
doi10.1145/3285029 +
titleDeep Learning Based Recommender System: A Survey and New Perspectives +
year2019 +