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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Scott Sanner, Krisztian Balog, Filip Radlinski, et al.
(2023), pp. 890-896
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Recommender Systems in the Era of Large Language Models (LLMs)
Zihuai Zhao, Wenqi Fan, Jiatong Li, et al.
IEEE Transactions on Knowledge and Data Engineering (2024) Vol. 36, Iss. 11, pp. 6889-6907
Open Access | Times Cited: 63
A survey on large language models for recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, et al.
World Wide Web (2024) Vol. 27, Iss. 5
Closed Access | Times Cited: 41
How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi, et al.
ACM transactions on office information systems (2024)
Open Access | Times Cited: 26
Trustworthy Recommender Systems
Shoujin Wang, Xiuzhen Zhang, Yan Wang, et al.
ACM Transactions on Intelligent Systems and Technology (2023) Vol. 15, Iss. 4, pp. 1-20
Open Access | Times Cited: 39
Large Language Models versus Natural Language Understanding and Generation
Nikitas N. Karanikolas, Eirini Manga, Nikoletta E. Samaridi, et al.
(2023), pp. 278-290
Open Access | Times Cited: 25
GenRec: Large Language Model for Generative Recommendation
Jianchao Ji, Zelong Li, Shuyuan Xu, et al.
Lecture notes in computer science (2024), pp. 494-502
Closed Access | Times Cited: 12
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 6448-6458
Open Access | Times Cited: 11
Generative News Recommendation
Shen Gao, Jiabao Fang, Quan Tu, et al.
Proceedings of the ACM Web Conference 2022 (2024), pp. 3444-3453
Open Access | Times Cited: 7
RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM Agents
Yu‐Bo Shu, H. P. Zhang, Hansu Gu, et al.
IEEE Transactions on Computational Social Systems (2024) Vol. 11, Iss. 5, pp. 6759-6770
Open Access | Times Cited: 6
Extracting Implicit User Preferences in Conversational Recommender Systems Using Large Language Models
Woo Seok Kim, Seongho Lim, Gun-Woo Kim, et al.
Mathematics (2025) Vol. 13, Iss. 2, pp. 221-221
Open Access
Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin, et al.
Lecture notes in computer science (2025), pp. 1-13
Closed Access
Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
Lei Guo, Ziang Lu, Junliang Yu, et al.
Proceedings of the ACM Web Conference 2022 (2024), pp. 3139-3149
Open Access | Times Cited: 4
Large Language Models for Intent-Driven Session Recommendations
Zhu Sun, Hongyang Liu, Xinghua Qu, et al.
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2024), pp. 324-334
Open Access | Times Cited: 4
A Review of Text Generation Based on Large Language Model
津睿 刘
Artificial Intelligence and Robotics Research (2025) Vol. 14, Iss. 01, pp. 194-208
Closed Access
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation
Zhankui He, Zhouhang Xie, Harald Steck, et al.
(2025), pp. 866-875
Closed Access
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
Yashar Deldjoo, Tommaso Di Noia
ACM Transactions on Intelligent Systems and Technology (2025)
Closed Access
Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin, et al.
ACM Transactions on Recommender Systems (2025)
Open Access
Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis
Lanling Xu, Junjie Zhang, B. Li, et al.
ACM Transactions on Knowledge Discovery from Data (2025)
Closed Access
Learning Human Feedback from Large Language Models for Content Quality-aware Recommendation
Huili Wang, Chuhan Wu, Yongfeng Huang, et al.
ACM transactions on office information systems (2025)
Closed Access
Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
Sein Kim, Hongseok Kang, S. K. Choi, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 1395-1406
Open Access | Times Cited: 3
LLM-generated Explanations for Recommender Systems
Sebastian Lubos, Thi Ngoc Trang Tran, Alexander Felfernig, et al.
(2024), pp. 276-285
Closed Access | Times Cited: 1
Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation
Wenxuan Zhang, Hongzhi Liu, Zhijin Dong, et al.
Lecture notes in computer science (2024), pp. 280-294
Closed Access | Times Cited: 1
Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations
Alessandro Petruzzelli, Cataldo Musto, L. Laraspata, et al.
(2024), pp. 298-308
Open Access | Times Cited: 1
Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domain
Cataldo Musto, Alessandro Francesco Maria Martina, Andrea Iovine, et al.
Journal of Intelligent Information Systems (2023) Vol. 62, Iss. 2, pp. 575-599
Open Access | Times Cited: 3
Empirical Evaluation of Large Language Models in Resume Classification
Prasanna Kumar R, M Rithani, Bharathi Mohan G, et al.
(2024), pp. 1-4
Closed Access
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