Fetching the paper…
Reading the bibliography…
In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions.
Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
Jock A Blackard and Denis J Dean · 1999
Earlier work this paper cites.
The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
Earlier work this paper cites.
Efficiently guiding imitation learning agents with human gaze
Akanksha Saran, Ruohan Zhang, Elaine Schaertl Short, and Scott Niekum · 2002
Earlier work this paper cites.
Data sparsity issues in the collaborative filtering framework
Miha Grčar, Dunja Mladenič, Blaž Fortuna, and Marko Grobelnik · 2005
Earlier work this paper cites.
The epoch-greedy algorithm for contextual multi-armed bandits
John Langford and Tong Zhang · 2007
Earlier work this paper cites.
Collaborative filtering recommender systems
J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen · 2007
Earlier work this paper cites.
Learning classifiers from only positive and unlabeled data
Charles Elkan and Keith Noto · 2008
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets
Yifan Hu, Yehuda Koren, and Chris Volinsky · 2008
Earlier work this paper cites.
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
Earlier work this paper cites.
On caption bias in interleaving experiments
Katja Hofmann, Fritz Behr, and Filip Radlinski · 2012
Earlier work this paper cites.
The impact of demographics (age and gender) and other user-characteristics on evaluating recommender systems
Joeran Beel, Stefan Langer, Andreas Nürnberger, and Marcel Genzmehr · 2013
Earlier work this paper cites.
Modeling dwell time to predict click-level satisfaction
Youngho Kim, Ahmed Hassan, Ryen W White, and Imed Zitouni · 2014
Earlier work this paper cites.
Missed diagnosis of stroke in the emergency department: a cross-sectional analysis of a large population-based sample
David E Newman-Toker, Ernest Moy, Ernest Valente, Rosanna Coffey, and Anika L Hines · 2014
Earlier work this paper cites.
Exploring the filter bubble: the effect of using recommender systems on content diversity
Tien T Nguyen, Pik-Mai Hui, F Maxwell Harper, Loren Terveen, and Joseph A Konstan · 2014
Earlier work this paper cites.
Beyond clicks: dwell time for personalization
Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan · 2014
Earlier work this paper cites.
Sex differences in structural organization of motor systems and their dissociable links with repetitive/restricted behaviors in children with autism
Kaustubh Supekar and Vinod Menon · 2015
Earlier work this paper cites.
2012-2016 Facebook Posts
Patrick Martinchek · 2016
Earlier work this paper cites.
Reactions now available globally
Meta · 2016
Earlier work this paper cites.
Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
Cited alongside, same era.
Social media engagement: What motivates user participation and consumption on YouTube?
M Laeeq Khan · 2017
Cited alongside, same era.
Returning is believing: Optimizing long-term user engagement in recommender systems
Qingyun Wu, Hongning Wang, Liangjie Hong, and Yue Shi · 2017
Cited alongside, same era.
All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness
Michael D Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera · 2018
Cited alongside, same era.
Human gaze following for human-robot interaction
Akanksha Saran, Srinjoy Majumdar, Elaine Schaertl Short, Andrea Thomaz, and Scott Niekum · 2018
Cited alongside, same era.
Measuring bias in consumer lending
Will Dobbie, Andres Liberman, Daniel Paravisini, and Vikram Pathania · 2021
Later among the works it cites.
X2T: training an x-to-text typing interface with online learning from user feedback
Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca D. Dragan, and Sergey Levine · 2021
Later among the works it cites.
Facebook tried to make its platform a healthier place. it got angrier instead
Keach Hagey and Jeff Jeff Horwitz · 2021
Later among the works it cites.
Online learning: A comprehensive survey
Steven CH Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao · 2021
Later among the works it cites.
A review of content-based and context-based recommendation systems
Umair Javed, Kamran Shaukat, Ibrahim A Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo · 2021
Later among the works it cites.
Five points for anger, one for a ‘like’: How facebook’s formula fostered rage and misinformation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, and Enhong Chen · 2019
Cited alongside, same era.
Trends in content-based recommendation
Pasquale Lops, Dietmar Jannach, Cataldo Musto, Toine Bogers, and Marijn Koolen · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
How good your recommender system is? A survey on evaluations in recommendation
Thiago Silveira, Min Zhang, Xiao Lin, Yiqun Liu, and Shaoping Ma · 2019
Cited alongside, same era.
Learning from positive and unlabeled data: A survey
Jessa Bekker and Jesse Davis · 2020
Cited alongside, same era.
Survey on applications of multi-armed and contextual bandits
Djallel Bouneffouf, Irina Rish, and Charu Aggarwal · 2020
Cited alongside, same era.
Measuring the diversity of facebook reactions to research
Cole Freeman, Hamed Alhoori, and Murtuza Shahzad · 2020
Cited alongside, same era.
Jeremy Merrill and Will Oremus · 2021
Later among the works it cites.
You won’t believe what’s in this paper! Clickbait, relevance and the curiosity gap
Kate Scott · 2021
Later among the works it cites.
Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue
Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua · 2021
Later among the works it cites.
Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro, and Ida Momennejad · 2021
Later among the works it cites.
Finite-sample regret bound for distributionally robust offline tabular reinforcement learning
Zhengqing Zhou, Zhengyuan Zhou, Qinxun Bai, Linhai Qiu, Jose Blanchet, and Peter Glynn · 2021
Later among the works it cites.
Generative adversarial reward learning for generalized behavior tendency inference
Xiaocong Chen, Lina Yao, Xianzhi Wang, Aixin Sun, and Quan Z Sheng · 2022
Closest in time.
Interaction-grounded learning for recommender systems
Jessica Maghakian, Kishan Panaganti, Paul Mineiro, Akanksha Saran, and Cheng Tan · 2022
Closest in time.
Revisiting popularity and demographic biases in recommender evaluation and effectiveness
Nicola Neophytou, Bhaskar Mitra, and Catherine Stinson · 2022
Closest in time.
Advancing care and outcomes for african american patients with multiple sclerosis
Annette F Okai, Annette M Howard, Mitzi J Williams, Justine D Brink, Chiayi Chen, Tamela L Stuchiner, Elizabeth Baraban, Grace Jeong, and Stanley L Cohan · 2022
Closest in time.
Robust reinforcement learning using offline data
Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, and Mohammad Ghavamzadeh · 2022
Closest in time.
Gendered information in resumes and its role in algorithmic and human hiring bias
Prasanna Parasurama and João Sedoc · 2022
Closest in time.
Understanding acoustic patterns of human teachers demonstrating manipulation tasks to robots
Akanksha Saran, Kush Desai, Mai Lee Chang, Rudolf Lioutikov, Andrea Thomaz, and Scott Niekum · 2022
Closest in time.
Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, and John Langford · 2022
Closest in time.