Fetching the paper…
Reading the bibliography…
Recommender systems (RSs) are software tools and algorithms developed to alleviate the problem of information overload, which makes it difficult for a user to make right decisions.
Using collaborative filtering to weave an information tapestry
David Goldberg, David Nichols, Brian M Oki, and Douglas Terry · 1992
Earlier work this paper cites.
Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
Earlier work this paper cites.
Recommender systems
Paul Resnick and Hal R Varian · 1997
Earlier work this paper cites.
Implicit feedback for recommender systems
Douglas W Oard, Jinmook Kim, et al · 1998
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
Energy-efficient communication protocol for wireless microsensor networks
Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan · 2000
Earlier work this paper cites.
Improving recommendation diversity
Keith Bradley and Barry Smyth · 2001
Earlier work this paper cites.
Wireless sensor networks: a survey
Ian F Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci · 2002
Earlier work this paper cites.
An application-specific protocol architecture for wireless microsensor networks
Wendi B Heinzelman, Anantha P Chandrakasan, and Hari Balakrishnan · 2002
Earlier work this paper cites.
Sep: A stable election protocol for clustered heterogeneous wireless sensor networks
Georgios Smaragdakis, Ibrahim Matta, and Azer Bestavros · 2004
Earlier work this paper cites.
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
Gediminas Adomavicius and Alexander Tuzhilin · 2005
Earlier work this paper cites.
Improving recommendation lists through topic diversification
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen · 2005
Earlier work this paper cites.
A survey on clustering algorithms for wireless sensor networks
Ameer Ahmed Abbasi and Mohamed Younis · 2007
Earlier work this paper cites.
Content-based recommendation systems
Michael J Pazzani and Daniel Billsus · 2007
Earlier work this paper cites.
An experimental comparison of click position-bias models
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey · 2008
Cited alongside, same era.
Understanding choice overload in recommender systems
Dirk Bollen, Bart P Knijnenburg, Martijn C Willemsen, and Mark Graus · 2010
Cited alongside, same era.
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
Cited alongside, same era.
Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira · 2011
Cited alongside, same era.
A comprehensive survey of neighborhood-based recommendation methods
Christian Desrosiers and George Karypis · 2011
Cited alongside, same era.
Evolution of wireless sensor networks towards the internet of things: A survey
Luca Mainetti, Luigi Patrono, and Antonio Vilei · 2011
Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Later among the works it cites.
Deep reinforcement learning for list-wise recommendations
Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, and Jiliang Tang · 2017
Later among the works it cites.
Deep reinforcement learning for page-wise recommendations
Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang · 2018
Later among the works it cites.
Recommendations with negative feedback via pairwise deep reinforcement learning
Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, and Dawei Yin · 2018
Later among the works it cites.
Stabilizing reinforcement learning in dynamic environment with application to online recommendation
Shi-Yong Chen, Yang Yu, Qing Da, Jun Tan, Hai-Kuan Huang, and Hai-Hong Tang · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Content-based recommender systems: State of the art and trends
Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro · 2011
Cited alongside, same era.
Deep content-based music recommendation
Aäron Van Den Oord, Sander Dieleman, and Benjamin Schrauwen · 2013
Cited alongside, same era.
Clustering in sensor networks: A literature survey
M Mehdi Afsar and Mohammad-H Tayarani-N · 2014
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Cited alongside, same era.
Collaborative deep learning for recommender systems
Hao Wang, Naiyan Wang, and Dit-Yan Yeung · 2015
Cited alongside, same era.
Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay · 2019
Later among the works it cites.
A load-balanced cross-layer design for energy-harvesting sensor networks
M Mehdi Afsar and Mohamed Younis · 2019
Later among the works it cites.
Recsim: A configurable simulation platform for recommender systems
Eugene Ie, Chih-wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier · 2019
Later among the works it cites.
Reinforcement learning to diversify top-n recommendation
Lixin Zou, Long Xia, Zhuoye Ding, Dawei Yin, Jiaxing Song, and Weidong Liu · 2019
Later among the works it cites.
Choice overload and recommendation effectiveness in related-article recommendations
Felix Beierle, Akiko Aizawa, Andrew Collins, and Joeran Beel · 2019
Later among the works it cites.
Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Morgane Lustman, Vince Gatto, Paul Covington, et al · 2019
Later among the works it cites.
Applications of wireless sensor networks: an up-to-date survey
Dionisis Kandris, Christos Nakas, Dimitrios Vomvas, and Grigorios Koulouras · 2020
Later among the works it cites.
Clustering objectives in wireless sensor networks: A survey and research direction analysis
Amin Shahraki, Amir Taherkordi, Øystein Haugen, and Frank Eliassen · 2020
Later among the works it cites.
Reinforcement learning based recommender systems: A survey
M Mehdi Afsar, Trafford Crump, and Behrouz Far · 2021
Closest in time.