Fetching the paper…
Reading the bibliography…
It has long been recognized that it is not enough for a Recommender System (RS) to provide recommendations based only on their relevance to users.
The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval . 335–336
Jaime Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Similarity vs. diversity. In International conference on case-based reasoning . Springer, 347–361
Barry Smyth and Paul McClave. 2001 · 2001
Earlier work this paper cites.
Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web . 22–32
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005 · 2005
Earlier work this paper cites.
Enhancing the diversity of conversational collaborative recommendations: a comparison
John Paul Kelly and Derek Bridge. 2006 · 2006
Earlier work this paper cites.
Novelty and diversity in information retrieval evaluation. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval . 659–666
Charles LA Clarke, Maheedhar Kolla, Gordon V Cormack, Olga Vechtomova, Azin Ashkan, Stefan Büttcher, and Ian MacKinnon. 2008 · 2008
Earlier work this paper cites.
Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM conference on Recommender systems . 123–130
Mi Zhang and Neil Hurley. 2008 · 2008
Earlier work this paper cites.
Diversifying search results. In Proceedings of the second ACM international conference on web search and data mining . 5–14
Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009 · 2009
Earlier work this paper cites.
Optimizing multiple objectives in collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems . 55–62
Tamas Jambor and Jun Wang. 2010 · 2010
Earlier work this paper cites.
Fast als-based matrix factorization for explicit and implicit feedback datasets. In Proceedings of the fourth ACM conference on Recommender systems . 71–78
István Pilászy, Dávid Zibriczky, and Domonkos Tikk. 2010 · 2010
Earlier work this paper cites.
A similarity measure for indefinite rankings
William Webber, Alistair Moffat, and Justin Zobel. 2010 · 2010
Earlier work this paper cites.
Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems . 109–116
Saúl Vargas and Pablo Castells. 2011 · 2011
Earlier work this paper cites.
Intent-oriented diversity in recommender systems. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval . 1211–1212
Saul Vargas, Pablo Castells, and David Vallet. 2011 · 2011
Earlier work this paper cites.
Pareto-efficient hybridization for multi-objective recommender systems. In Proceedings of the sixth ACM conference on Recommender systems . 19–26
Marco Tulio Ribeiro, Anisio Lacerda, Adriano Veloso, and Nivio Ziviani. 2012 · 2012
Earlier work this paper cites.
Adaptive diversification of recommendation results via latent factor portfolio. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval . 175–184
Yue Shi, Xiaoxue Zhao, Jun Wang, Martha Larson, and Alan Hanjalic. 2012 · 2012
Earlier work this paper cites.
Explicit relevance models in intent-oriented information retrieval diversification. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval . 75–84
Saúl Vargas, Pablo Castells, and David Vallet. 2012 · 2012
Earlier work this paper cites.
Personalised ranking with diversity. In Proceedings of the 7th ACM Conference on Recommender Systems . 379–382
Neil J Hurley. 2013 · 2013
Earlier work this paper cites.
Set-oriented personalized ranking for diversified top-n recommendation. In Proceedings of the 7th ACM Conference on Recommender Systems . 415–418
Ruilong Su, Li’Ang Yin, Kailong Chen, and Yong Yu. 2013 · 2013
Earlier work this paper cites.
User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems . 161–168
Michael D Ekstrand, F Maxwell Harper, Martijn C Willemsen, and Joseph A Konstan. 2014 · 2014
Earlier work this paper cites.
Coverage, redundancy and size-awareness in genre diversity for recommender systems. In Proceedings of the 8th ACM Conference on Recommender systems . 209–216
Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, and Pablo Castells. 2014 · 2014
Earlier work this paper cites.
Novelty and diversity evaluation and enhancement in recommender systems
Saúl Vargas. 2015 · 2015
Earlier work this paper cites.
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
Sander Greenland, Stephen J Senn, Kenneth J Rothman, John B Carlin, Charles Poole, Steven N Goodman, and Douglas G Altman. 2016 · 2016
Earlier work this paper cites.
Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems
Marius Kaminskas and Derek Bridge. 2016 · 2016
Earlier work this paper cites.
Adaptive multi-attribute diversity for recommender systems
Tommaso Di Noia, Jessica Rosati, Paolo Tomeo, and Eugenio Di Sciascio. 2017 · 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 · 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.
Practical diversified recommendations on youtube with determinantal point processes. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 2165–2173
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H Chi, and Jennifer Gillenwater. 2018 · 2018
Earlier work this paper cites.
Subprofile-aware diversification of recommendations
Mesut Kaya and Derek Bridge. 2019 · 2019
Cited alongside, same era.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 2019
Cited alongside, same era.
Active learning in recommender systems: an unbiased and beyond-accuracy perspective
Diego Carraro. 2020 · 2020
Cited alongside, same era.
Novelty and diversity in recommender systems
Pablo Castells, Neil Hurley, and Saul Vargas. 2021 · 2021
Cited alongside, same era.
Generate natural language explanations for recommendation
Hanxiong Chen, Xu Chen, Shaoyun Shi, and Yongfeng Zhang. 2021 · 2021
Cited alongside, same era.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, and Julian McAuley. 2023a · 2023
Later among the works it cites.
GPT4Rec: A generative framework for personalized recommendation and user interests interpretation
Jinming Li, Wentao Zhang, Tian Wang, Guanglei Xiong, Alan Lu, and Gerard Medioni. 2023d · 2023
Later among the works it cites.
Personalized prompt learning for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2023b · 2023
Later among the works it cites.
A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News
Xinyi Li, Yongfeng Zhang, and Edward C Malthouse. 2023c · 2023
Later among the works it cites.
How Can Recommender Systems Benefit from Large Language Models: A Survey
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, and Hao Wang. 2021 · 2021
Cited alongside, same era.
ReXPlug: Explainable Recommendation Using Plug-and-Play Language Model. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 81–91
Deepesh V. Hada, Vijaikumar M., and Shirish K. Shevade. 2021 · 2021
Cited alongside, same era.
Pre-trained language model for web-scale retrieval in baidu search. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 3365–3375
Yiding Liu, Weixue Lu, Suqi Cheng, Daiting Shi, Shuaiqiang Wang, Zhicong Cheng, and Dawei Yin. 2021 · 2021
Cited alongside, same era.
UNBERT: User-News Matching BERT for News Recommendation.. In IJCAI . 3356–3362
Qi Zhang, Jingjie Li, Qinglin Jia, Chuyuan Wang, Jieming Zhu, Zhaowei Wang, and Xiuqiang He. 2021 · 2021
Cited alongside, same era.
Language models are realistic tabular data generators
Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. 2022 · 2022
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.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems . 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al · 2023
Later among the works it cites.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023b · 2023
Later among the works it cites.
A First Look at LLM-Powered Generative News Recommendation
Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu. 2023a · 2023
Later among the works it cites.
Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023 · 2023
Later among the works it cites.
Large Language Model Augmented Narrative Driven Recommendations
Sheshera Mysore, Andrew McCallum, and Hamed Zamani. 2023 · 2023
Later among the works it cites.
Generative Sequential Recommendation with GPTRec
Aleksandr V Petrov and Craig Macdonald. 2023 · 2023
Later among the works it cites.
Sajjad Rahmani, AmirHossein Naghshzan, and Latifa Guerrouj. 2023 · 2023
Later among the works it cites.
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
On the planning abilities of large language models-a critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati. 2023 · 2023
Later among the works it cites.
Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
Lei Wang and Ee-Peng Lim. 2023 · 2023
Later among the works it cites.
A survey on the fairness of recommender systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023 · 2023
Later among the works it cites.
A survey on model compression and acceleration for pretrained language models. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 10566–10575
Canwen Xu and Julian McAuley. 2023 · 2023
Later among the works it cites.
Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Later among the works it cites.
Prompt learning for news recommendation
Zizhuo Zhang and Bang Wang. 2023 · 2023
Later among the works it cites.
Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
Anthropic. 2024 · 2024
Closest in time.
Chatbot arena: An open platform for evaluating llms by human preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E Gonzalez, et al · 2024
Closest in time.
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, and Mao Yang. 2024 · 2024
Closest in time.
Retrieval-Augmented Generation for Large Language Models: A Survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang. 2024 · 2024
Closest in time.
Large Language Models are Zero-Shot Rankers for Recommender Systems. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part II . Springer-Verlag, Berlin, Heidelberg, 364–381
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024 · 2024
Closest in time.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
Closest in time.