Fetching the paper…
Reading the bibliography…
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.
Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
Earlier work this paper cites.
The yahoo! music dataset and kdd-cup’11
Gideon Dror, Noam Koenigstein, Yehuda Koren, and Markus Weimer · 2012
Earlier work this paper cites.
Bpr: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Improving pairwise learning for item recommendation from implicit feedback
Steffen Rendle and Christoph Freudenthaler · 2014
Earlier work this paper cites.
Leveraging social connections to improve personalized ranking for collaborative filtering
Tong Zhao, Julian McAuley, and Irwin King · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk · 2016
Earlier work this paper cites.
What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
Earlier work this paper cites.
Improved recurrent neural networks for session-based recommendations
Yong Kiam Tan, Xinxing Xu, and Yong Liu · 2016
Earlier work this paper cites.
Lambdafm: learning optimal ranking with factorization machines using lambda surrogates
Fajie Yuan, Guibing Guo, Joemon M Jose, Long Chen, Haitao Yu, and Weinan Zhang · 2016
Earlier work this paper cites.
Neural factorization machines for sparse predictive analytics
Xiangnan He and Tat-Seng Chua · 2017
Earlier work this paper cites.
Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Dropoutnet: Addressing cold start in recommender systems
Maksims Volkovs, Guangwei Yu, and Tomi Poutanen · 2017
Earlier work this paper cites.
Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang · 2017
Earlier work this paper cites.
Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley · 2018
Earlier work this paper cites.
xdeepfm: Combining explicit and implicit feature interactions for recommender systems
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun · 2018
Earlier work this paper cites.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi · 2018
Earlier work this paper cites.
Entire space multi-task model: An effective approach for estimating post-click conversion rate
Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai · 2018
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
Cited alongside, same era.
Deep interest network for click-through rate prediction
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai · 2018
Cited alongside, same era.
Are we really making much progress? a worrying analysis of recent neural recommendation approaches
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach · 2019
Cited alongside, same era.
On the difficulty of evaluating baselines: A study on recommender systems
Steffen Rendle, Li Zhang, and Yehuda Koren · 2019
Learning transferable user representations with sequential behaviors via contrastive pre-training
Mingyue Cheng, Fajie Yuan, Qi Liu, Xin Xin, and Enhong Chen · 2021
Later among the works it cites.
An empirical study identifying bias in Yelp dataset
Seri Choi et al · 2021
Later among the works it cites.
Progress in recommender systems research: Crisis? what crisis?
Paolo Cremonesi and Dietmar Jannach · 2021
Later among the works it cites.
A troubling analysis of reproducibility and progress in recommender systems research
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, and Dietmar Jannach · 2021
Later among the works it cites.
A case study on sampling strategies for evaluating neural sequential item recommendation models
Alexander Dallmann, Daniel Zoller, and Andreas Hotho · 2021
Later among the works it cites.
Product based recommendation system on amazon dataset
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang · 2019
Cited alongside, same era.
Neural graph collaborative filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua · 2019
Cited alongside, same era.
A simple convolutional generative network for next item recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He · 2019
Cited alongside, same era.
Deep interest evolution network for click-through rate prediction
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai · 2019
Cited alongside, same era.
Adaptive factorization network: Learning adaptive-order feature interactions
Weiyu Cheng, Yanyan Shen, and Linpeng Huang · 2020
Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang · 2020
Cited alongside, same era.
A critical study on data leakage in recommender system offline evaluation
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li · 2020
Cited alongside, same era.
ROHIT DWIVEDI et al · 2021
Later among the works it cites.
Persia: A hybrid system scaling deep learning based recommenders up to 100 trillion parameters
Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, et al · 2021
Later among the works it cites.
C ˆ2-rec: An effective consistency constraint for sequential recommendation
Chong Liu, Xiaoyang Liu, Rongqin Zheng, Lixin Zhang, Xiaobo Liang, Juntao Li, Lijun Wu, Min Zhang, and Leyu Lin · 2021
Later among the works it cites.
Empirical analysis of session-based recommendation algorithms
Malte Ludewig, Noemi Mauro, Sara Latifi, and Dietmar Jannach · 2021
Later among the works it cites.
One model to serve all: Star topology adaptive recommender for multi-domain ctr prediction
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, et al · 2021
Later among the works it cites.
One4all user representation for recommender systems in e-commerce
Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Minkyu Kim, Young-Jin Park, Jisu Jeong, and Seungjae Jung · 2021
Later among the works it cites.
Scaling law for recommendation models: Towards general-purpose user representations
Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Su Young Kim, and Max Nihlen Ramstrom · 2021
Later among the works it cites.
Fm2: Field-matrixed factorization machines for recommender systems
Yang Sun, Junwei Pan, Alex Zhang, and Aaron Flores · 2021
Later among the works it cites.
Stackrec: Efficient training of very deep sequential recommender models by iterative stacking
Jiachun Wang, Fajie Yuan, Jian Chen, Qingyao Wu, Chengmin Li, Min Yang, Yang Sun, and Guoxiao Zhang · 2021
Later among the works it cites.
Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi · 2021
Later among the works it cites.
One person, one model, one world: Learning continual user representation without forgetting
Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, and Yudong Li · 2021
Later among the works it cites.
Cross-domain recommendation: challenges, progress, and prospects
Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu · 2021
Later among the works it cites.
Open benchmarking for click-through rate prediction
Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He · 2021
Later among the works it cites.
Top-n recommendation algorithms: A quest for the state-of-the-art
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, and Claudio Pomo · 2022
Closest in time.
Transrec: Learning transferable recommendation from mixture-of-modality feedback
Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M Jose, Chenyun Yu, Beibei Kong, Zhijin Wang, Bo Hu, and Zang Li · 2022
Closest in time.
Where to go next for recommender systems? id- vs. modality-based recommender models revisited
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Fei Yang, and Yunzhu Pan · 2022
Closest in time.
Filter-enhanced mlp is all you need for sequential recommendation
Kun Zhou, Hui Yu, Wayne Xin Zhao, and Ji-Rong Wen · 2022
Closest in time.
Bars: Towards open benchmarking for recommender systems
Jieming Zhu, Kelong Mao, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Zhicheng Dou, Xi Xiao, and Rui Zhang · 2022
Closest in time.