Fetching the paper…
Reading the bibliography…
The goal of session-based recommendation (SR) models is to utilize the information from past actions (e.g.
Item popularity and recommendation accuracy. In Proceedings of the fifth ACM conference on Recommender systems . ACM, 125–132
Harald Steck. 2011 · 2011
Earlier work this paper cites.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015 · 2015
Earlier work this paper cites.
A comparative study on regularization strategies for embedding-based neural networks
Hao Peng, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu, and Zhi Jin. 2015 · 2015
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR-2016)
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems . ACM, 42–46
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
Earlier work this paper cites.
When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems . ACM, 306–310
Dietmar Jannach and Malte Ludewig. 2017 · 2017
Earlier work this paper cites.
Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Normface: L2 hypersphere embedding for face verification. In Proceedings of the 25th ACM international conference on Multimedia . ACM, 1041–1049
Feng Wang, Xiang Xiang, Jian Cheng, and Alan Loddon Yuille. 2017 · 2017
Cited alongside, same era.
STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 1831–1839
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018 · 2018
Cited alongside, same era.
Ring loss: Convex feature normalization for face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 5089–5097
Yutong Zheng, Dipan K Pal, and Marios Savvides. 2018 · 2018
Later among the works it cites.
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019 · 2019
Closest in time.
Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19) . ACM, 1069–1072
Diksha Garg, Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff. 2019 · 2019
Closest in time.
A Collaborative Session-based Recommendation Approach with Parallel Memory Modules. In Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19) . ACM, 345–354
Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. In Proceedings of the 12th ACM Conference on Recommender Systems . ACM, 279–287
Longqi Yang, Yin Cui, Yuan Xuan, Chenyang Wang, Serge Belongie, and Deborah Estrin. 2018 · 2018
Cited alongside, same era.
Closest in time.
Session-based Recommendation with Graph Neural Networks. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019 · 2019
Closest in time.