Fetching the paper…
Reading the bibliography…
Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language model techniques to capture user interests and understand news content.
Embedding-based news recommendation for millions of users. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 1933–1942
Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. 2017 · 1942
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Exploring the filter bubble: the effect of using recommender systems on content diversity. In Proceedings of the 23rd international conference on World wide web . 677–686
Tien T Nguyen, Pik-Mai Hui, F Maxwell Harper, Loren Terveen, and Joseph A Konstan. 2014 · 2014
Earlier work this paper cites.
Convolutional neural network for sentence classification
Yahui Chen. 2015 · 2015
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
The Expanding News Desert, Center for Innovation and Sustainability in Local Media
PM Abernathy. 2018 · 2018
Earlier work this paper cites.
Balanced neighborhoods for multi-sided fairness in recommendation. In Conference on fairness, accountability and transparency . PMLR, 202–214
Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018 · 2018
Earlier work this paper cites.
Facebook’s Latest Algorithm Change: Here Are The News Sites That Stand To Lose The Most
Kathleen Chaykowski. 2018 · 2018
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 · 2018
Earlier work this paper cites.
Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach.. In IJCAI . 3805–3811
Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
The spread of true and false news online
Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018 · 2018
Earlier work this paper cites.
Neural news recommendation with long-and short-term user representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 336–345
Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, and Xing Xie. 2019 · 2019
Earlier work this paper cites.
Understanding LSTM–a tutorial into long short-term memory recurrent neural networks
Ralf C Staudemeyer and Eric Rothstein Morris. 2019 · 2019
Cited alongside, same era.
Neural news recommendation with attentive multi-view learning
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019b · 2019
Cited alongside, same era.
Neural news recommendation with attentive multi-view learning
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019c · 2019
Cited alongside, same era.
Neural news recommendation with multi-head self-attention. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) . 6389–6394
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, and Xing Xie. 2019d · 2019
Cited alongside, same era.
Personalized prompt learning for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2022 · 2022
Later among the works it cites.
ProFairRec: Provider fairness-aware news recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1164–1173
Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2022 · 2022
Later among the works it cites.
Is News Recommendation a Sequential Recommendation Task?. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2382–2386
Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li, and Yongfeng Huang. 2022 · 2022
Later among the works it cites.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Opportunistic multi-aspect fairness through personalized re-ranking. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization . 239–247
Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, and Bamshad Mobasher. 2020 · 2020
Cited alongside, same era.
Mind: A large-scale dataset for news recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 3597–3606
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, et al · 2020
Cited alongside, same era.
User-centered evaluation of popularity bias in recommender systems. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization . 119–129
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, and Edward Malthouse. 2021 · 2021
Cited alongside, same era.
The echo chamber effect on social media
Matteo Cinelli, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2021 · 2021
Cited alongside, same era.
Woojeong Jin, Yu Cheng, Yelong Shen, Weizhu Chen, and Xiang Ren. 2021 · 2021
Cited alongside, same era.
Towards personalized fairness based on causal notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1054–1063
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2021 · 2021
Cited alongside, same era.
Language Models as Recommender Systems: Evaluations and Limitations. In I (Still) Can’t Believe It’s Not Better! NeurIPS 2021 Workshop
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang. 2021 · 2021
Cited alongside, same era.
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
Cited alongside, same era.
A Preliminary Investigation of Fake Peer-Reviewed Citations and References Generated by ChatGPT
Terence Day. 2023 · 2023
Closest in time.
Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions
Jocelyn Gravel, Madeleine D’Amours-Gravel, and Esli Osmanlliu. 2023a · 2023
Closest in time.
Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions
Jocelyn Gravel, Madeleine D’Amours-Gravel, and Esli Osmanlliu. 2023b · 2023
Closest in time.
Is ChatGPT a Good Recommender? A Preliminary Study
Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023 · 2023
Closest in time.
Lawyer apologizes for fake court citations from ChatGPT
Ramishah Maruf. 2023 · 2023
Closest in time.
Is ChatGPT a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
Closest in time.
ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope
Partha Pratim Ray. 2023 · 2023
Closest in time.
Exploring AI ethics of chatgpt: A diagnostic analysis
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023 · 2023
Closest in time.