Editing Recommender system (section)

=== Session-based recommender systems ===
These recommender systems use the interactions of a user within a session<ref name="a">{{cite arXiv|last1=Hidasi|first1=Balázs|last2=Karatzoglou|first2=Alexandros|last3=Baltrunas|first3=Linas|last4=Tikk|first4=Domonkos|date=2016-03-29|title=Session-based Recommendations with Recurrent Neural Networks|class=cs.LG|eprint=1511.06939}}</ref> to generate recommendations. Session-based recommender systems are used at YouTube<ref name="yt">{{cite arXiv|last1=Chen|first1=Minmin|last2=Beutel|first2=Alex|last3=Covington|first3=Paul|last4=Jain|first4=Sagar|last5=Belletti|first5=Francois|last6=Chi|first6=Ed|title=Top-K Off-Policy Correction for a REINFORCE Recommender System|year=2018|class=cs.LG|eprint=1812.02353}}</ref> and Amazon.<ref name="amzn">{{Cite book|last1=Yifei|first1=Ma|last2=Narayanaswamy|first2=Balakrishnan|last3=Haibin|first3=Lin|last4=Hao|first4=Ding|title=Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining |chapter=Temporal-Contextual Recommendation in Real-Time |year=2020|pages=2291–2299|publisher=Association for Computing Machinery|doi=10.1145/3394486.3403278|isbn=978-1-4503-7998-4|s2cid=221191348|doi-access=free}}</ref> These are particularly useful when history (such as past clicks, purchases) of a user is not available or not relevant in the current user session. Domains where session-based recommendations are particularly relevant include video, e-commerce, travel, music and more. Most instances of session-based recommender systems rely on the sequence of recent interactions within a session without requiring any additional details (historical, demographic) of the user. Techniques for session-based recommendations are mainly based on generative sequential models such as [[Recurrent neural network|recurrent neural networks]],<ref name="a" /><ref>{{Cite book|last1=Hidasi|first1=Balázs|last2=Karatzoglou|first2=Alexandros|title=Proceedings of the 27th ACM International Conference on Information and Knowledge Management |chapter=Recurrent Neural Networks with Top-k Gains for Session-based Recommendations |date=2018-10-17|chapter-url=https://doi.org/10.1145/3269206.3271761|series=CIKM '18|location=Torino, Italy|publisher=Association for Computing Machinery|pages=843–852|doi=10.1145/3269206.3271761|arxiv=1706.03847|isbn=978-1-4503-6014-2|s2cid=1159769}}</ref> [[Transformer (deep learning architecture)|transformers]],<ref>{{cite arXiv|last1=Kang|first1=Wang-Cheng|last2=McAuley|first2=Julian|title=Self-Attentive Sequential Recommendation|year=2018|class=cs.IR|eprint=1808.09781}}</ref> and other deep-learning-based approaches.<ref>{{Cite book|last1=Li|first1=Jing|last2=Ren|first2=Pengjie|last3=Chen|first3=Zhumin|last4=Ren|first4=Zhaochun|last5=Lian|first5=Tao|last6=Ma|first6=Jun|title=Proceedings of the 2017 ACM on Conference on Information and Knowledge Management |chapter=Neural Attentive Session-based Recommendation |date=2017-11-06|chapter-url=https://doi.org/10.1145/3132847.3132926|series=CIKM '17|location=Singapore, Singapore|publisher=Association for Computing Machinery|pages=1419–1428|doi=10.1145/3132847.3132926|arxiv=1711.04725|isbn=978-1-4503-4918-5|s2cid=21066930}}</ref><ref>{{Cite book|last1=Liu|first1=Qiao|last2=Zeng|first2=Yifu|last3=Mokhosi|first3=Refuoe|last4=Zhang|first4=Haibin|title=Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining |chapter=STAMP |date=2018-07-19|chapter-url=https://doi.org/10.1145/3219819.3219950|series=KDD '18|location=London, United Kingdom|publisher=Association for Computing Machinery|pages=1831–1839|doi=10.1145/3219819.3219950|isbn=978-1-4503-5552-0|s2cid=50775765}}</ref>