Donpech/LLM4Rec-Awesome-Papers

A list of awesome papers and resources of recommender system on large language model (LLM).

LLM for Recommendation Systems

A list of awesome papers and resources of recommender system on large language model (LLM).

🎉 News: Our LLM4Rec survey has been released. A Survey on Large Language Models for Recommendation

The related work and projects will be updated soon and continuously.

If our work has been of assistance to you, please feel free to cite our survey. Thank you.

@article{llm4recsurvey,
  author       = {Likang Wu and Zhi Zheng and Zhaopeng Qiu and Hao Wang and Hongchao Gu and Tingjia Shen and Chuan Qin and Chen Zhu and Hengshu Zhu and Qi Liu and Hui Xiong and Enhong Chen},
  title        = {A Survey on Large Language Models for Recommendation},
  journal      = {CoRR},
  volume       = {abs/2305.19860},
  year         = {2023}
}

Table of Contents

• The papers and related projects
  • No Tuning
  • Supervised Fine-Tuning
  • Related Survey
  • Common Datasets
• Single card (RTX 3090) debuggable generative language models that support Chinese corpus

The papers and related projects

No Tuning

Note: The tuning here only indicates whether the LLM model has been tuned.

Name	Paper	Venue	Year	Code	LLM
N/A	Large Language Models as Zero-Shot Conversational Recommenders, Zhankui He*, Zhouhang Xie*, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Majumder, Nathan Kallus, Julian McAuley, Conference on Information and Knowledge Management, CIKM'23	CIKM	2023	Python	GPT-3.5-turbo ,GPT-4,BAIZE,Vicuna
Agent4Rec	Zhang A, Sheng L, Chen Y, et al. On Generative Agents in Recommendation[J]. arXiv preprint arXiv:2310.10108, 2023.	arxiv	2023	Python	GPT4
N/A	Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging	arxiv	2023	N/A	BLIP-2+GPT4
InteRecAgent	Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations	arxiv	2023	N/A	GPT4
GPT4SM	Peng, Wenjun, et al. "Are GPT Embeddings Useful for Ads and Recommendation?." International Conference on Knowledge Science, Engineering and Management. Cham: Springer Nature Switzerland, 2023.	KSEM	2023	Python	GPT
LLMRG	Wang, Y., Chu, Z., Ouyang, X., Wang, S., Hao, H., Shen, Y., ... & Li, S. (2023). Enhancing Recommender Systems with Large Language Model Reasoning Graphs. arXiv preprint arXiv:2308.10835.	arxiv	2023	N/A	GPT-3.5/GPT4
RAH	Shu, Y., Gu, H., Zhang, P., Zhang, H., Lu, T., Li, D., & Gu, N. (2023). RAH! RecSys-Assistant-Human: A Human-Central Recommendation Framework with Large Language Models. arXiv preprint arXiv:2308.09904.	arxiv	2023	N/A	GPT4
LLM-Rec	Lyu, Hanjia, et al. "LLM-Rec: Personalized Recommendation via Prompting Large Language Models." arXiv preprint arXiv:2307.15780 (2023).	arxiv	2023	N/A	GPT-3
N/A	Sanner, S., Balog, K., Radlinski, F., Wedin, B., & Dixon, L. (2023). Large Language Models are Competitive Near Cold-start Recommenders for Language-and Item-based Preferences. arXiv preprint arXiv:2307.14225.	RecSys	2023	N/A	PaLM
MINT	Mysore S, McCallum A, Zamani H. Large Language Model Augmented Narrative Driven Recommendations[J]. arXiv preprint arXiv:2306.02250, 2023.	Recsys	2023	N/A	175B InstructGPT
KAR	Xi, Y., Liu, W., Lin, J., Zhu, J., Chen, B., Tang, R., ... & Yu, Y. (2023). Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models. arXiv preprint arXiv:2306.10933.	arxiv	2023	Python	ChatGLM
RecAgent	Wang, L., Zhang, J., Chen, X., Lin, Y., Song, R., Zhao, W. X., & Wen, J. R. (2023). RecAgent: A Novel Simulation Paradigm for Recommender Systems. arXiv preprint arXiv:2306.02552.	arxiv	2023	Python	ChatGPT
AnyPredict	Wang Z, Gao C, Xiao C, et al. AnyPredict: Foundation Model for Tabular Prediction[J]. arXiv preprint arXiv:2305.12081, 2023.	arxiv	2023	N/A	ChatGPT,BioBERT
iEvaLM	Wang, X., Tang, X., Zhao, W. X., Wang, J., & Wen, J. R. (2023). Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models. arXiv preprint arXiv:2305.13112.	arxiv	2023	Python	ChatGPT
N/A	Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., & Zhao, W. X. (2023). Large Language Models are Zero-Shot Rankers for Recommender Systems. arXiv preprint arXiv:2305.08845.	arxiv	2023	Python	ChatGPT
FaiRLLM	Zhang, J., Bao, K., Zhang, Y., Wang, W., Feng, F., & He, X. (2023). Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. arXiv preprint arXiv:2305.07609.	Recsys	2023	Python	ChatGPT
GENRE	Liu, Q., Chen, N., Sakai, T., & Wu, X. M. (2023). A First Look at LLM-Powered Generative News Recommendation. arXiv preprint arXiv:2305.06566.	arxiv	2023	Python	ChatGPT
N/A	Lin, G., & Zhang, Y. (2023). Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT. arXiv preprint arXiv:2305.04518.	arxiv	2023	N/A	ChatGPT
N/A	Dai, S., Shao, N., Zhao, H., Yu, W., Si, Z., Xu, C., ... & Xu, J. (2023). Uncovering ChatGPT's Capabilities in Recommender Systems. arXiv preprint arXiv:2305.02182.	arxiv	2023	Python	ChatGPT
N/A	Liu, J., Liu, C., Lv, R., Zhou, K., & Zhang, Y. (2023). Is ChatGPT a Good Recommender? A Preliminary Study. arXiv preprint arXiv:2304.10149.	arxiv	2023	N/A	ChatGPT
VQ-Rec	Hou Y, He Z, McAuley J, et al. Learning vector-quantized item representation for transferable sequential recommenders[C]//Proceedings of the ACM Web Conference 2023. 2023: 1162-1171.	ACM	2023	Python	BERT
RankGPT	Sun, W., Yan, L., Ma, X., Ren, P., Yin, D., & Ren, Z. (2023). Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent. arXiv preprint arXiv:2304.09542.	arxiv	2023	Python	ChatGPT/4
GeneRec	Wang, W., Lin, X., Feng, F., He, X., & Chua, T. S. (2023). Generative Recommendation: Towards Next-generation Recommender Paradigm. arXiv preprint arXiv:2304.03516.	arxiv	2023	Python	N/A
NIR	Wang, L., & Lim, E. P. (2023). Zero-Shot Next-Item Recommendation using Large Pretrained Language Models. arXiv preprint arXiv:2304.03153.	arxiv	2023	Python	GPT-3.5
Chat-REC	Gao, Y., Sheng, T., Xiang, Y., Xiong, Y., Wang, H., & Zhang, J. (2023). Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System. arXiv preprint arXiv:2303.14524.	arxiv	2023	N/A	ChatGPT
N/A	Sileo, D., Vossen, W., & Raymaekers, R. (2022, April). Zero-Shot Recommendation as Language Modeling. In Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022..	ECIR	2022	Python	GPT-2
UniCRS	Wang, X., Zhou, K., Wen, J. R., & Zhao, W. X. (2022, August). Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1929-1937).	KDD	2022	Python	GPT-2/ DialoGPT /BART
LLMRec	Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang. arXiv preprint arXiv:2311.00423, 2023.	WSDM	2024	Python	ChatGPT

Supervised Fine-Tuning

Name	Paper	Venue	Year	Code	LLM
TransRec	Lin X, Wang W, Li Y, et al. A Multi-facet Paradigm to Bridge Large Language Model and Recommendation[J]. arXiv preprint arXiv:2310.06491, 2023.	arxiv	2023	N/A	BART-large and LLaMA-7B
RecSysLLM	Chu, Z., Hao, H., Ouyang, X., Wang, S., Wang, Y., Shen, Y., ... & Li, S. (2023). Leveraging Large Language Models for Pre-trained Recommender Systems. arXiv preprint arXiv:2308.10837.	arxiv	2023	N/A	GLM-10B
BIGRec	A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems	arxiv	2023	Python	LLaMA
LLMCRS	Feng, Yue, et al. A Large Language Model Enhanced Conversational Recommender System. arXiv preprint arXiv:2308.06212 (2023).	arxiv	2023	N/A	Flan-T5/LLaMA
GLRec	Wu, L., Qiu, Z., Zheng, Z., Zhu, H., & Chen, E. (2023). Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations.	arxiv	2023	N/A	BELLE
GIRL	Zheng, Z., Qiu, Z., Hu, X., Wu, L., Zhu, H., & Xiong, H. (2023). Generative Job Recommendations with Large Language Model.	arxiv	2023	N/A	BELLE
Amazon-M2	Jin, Wei et al. Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation. ArXiv abs/2307.09688 (2023)	arxiv	2023	Project	mT5
GenRec	Ji, J., Li, Z., Xu, S., Hua, W., Ge, Y., Tan, J., & Zhang, Y. (2023). GenRec: Large Language Model for Generative Recommendation. arXiv e-prints, arXiv-2307.	arxiv	2023	Python	LLaMA
RecLLM	Friedman, L., Ahuja, S., Allen, D., Tan, T., Sidahmed, H., Long, C., ... & Tiwari, M. (2023). Leveraging Large Language Models in Conversational Recommender Systems. arXiv preprint arXiv:2305.07961.	arxiv	2023	N/A	LaMDA(video)
DPLLM	Carranza, A. G., Farahani, R., Ponomareva, N., Kurakin, A., Jagielski, M., & Nasr, M. (2023). Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models. arXiv preprint arXiv:2305.05973.	arxiv	2023	N/A	T5
PBNR	Li, X., Zhang, Y., & Malthouse, E. C. (2023). PBNR: Prompt-based News Recommender System. arXiv preprint arXiv:2304.07862.	arxiv	2023	N/A	T5
GPTRec	Petrov, A. V., & Macdonald, C. (2023). Generative Sequential Recommendation with GPTRec. arXiv preprint arXiv:2306.11114.	Gen-IR@SIGIR	2023	N/A	GPT-2
CTRL	Li X, Chen B, Hou L, et al. CTRL: Connect Tabular and Language Model for CTR Prediction[J]. arXiv preprint arXiv:2306.02841, 2023.	arxiv	2023	N/A	RoBERTa/GLM
UniTRec	Mao, Z., Wang, H., Du, Y., & Wong, K. F. (2023). UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation. arXiv preprint arXiv:2305.15756.	ACL	2023	Python	BART
ICPC	Christakopoulou, K., Lalama, A., Adams, C., Qu, I., Amir, Y., Chucri, S., ... & Chen, M. (2023). Large Language Models for User Interest Journeys. arXiv preprint arXiv:2305.15498.	arxiv	2023	N/A	LaMDA
TransRec	Fu, J., Yuan, F., Song, Y., Yuan, Z., Cheng, M., Cheng, S., ... & Pan, Y. (2023). Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights. arXiv preprint arXiv:2305.15036.	arxiv	2023	N/A	RoBERTa
N/A	Li, R., Deng, W., Cheng, Y., Yuan, Z., Zhang, J., & Yuan, F. (2023). Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights. arXiv preprint arXiv:2305.11700.	arxiv	2023	N/A	OPT
PALR	Chen, Z. (2023). PALR: Personalization Aware LLMs for Recommendation. arXiv preprint arXiv:2305.07622.	arxiv	2023	N/A	LLaMa
InstructRec	Zhang, J., Xie, R., Hou, Y., Zhao, W. X., Lin, L., & Wen, J. R. (2023). Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001.	arxiv	2023	N/A	FLAN-T5-3B
N/A	Kang, W. C., Ni, J., Mehta, N., Sathiamoorthy, M., Hong, L., Chi, E., & Cheng, D. Z. (2023). Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction. arXiv preprint arXiv:2305.06474.	arxiv	2023	N/A	FLAN/ChatGPT
LSH	Rahmani, S., Naghshzan, A., & Guerrouj, L. (2023). Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study. arXiv preprint arXiv:2305.03017.	arxiv	2023	N/A	BERT
TALLRec	Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., & He, X. (2023). TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. arXiv preprint arXiv:2305.00447.	arxiv	2023	Python	Llama-7B
GPT4Rec	Li, J., Zhang, W., Wang, T., Xiong, G., Lu, A., & Medioni, G. (2023). GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation. arXiv preprint arXiv:2304.03879.	arxiv	2023	N/A	GPT-2
IDvs.MoRec	Yuan, Z., Yuan, F., Song, Y., Li, Y., Fu, J., Yang, F., ... & Ni, Y. (2023). Where to go next for recommender systems? id-vs. modality-based recommender models revisited. arXiv preprint arXiv:2303.13835.	SIGIR	2023	Python	BERT
GReaT	Borisov, V., Seßler, K., Leemann, T., Pawelczyk, M., & Kasneci, G. (2022). Language models are realistic tabular data generators. arXiv preprint arXiv:2210.06280.	ICLR	2023	Python	GPT-2
M6-Rec	Cui, Z., Ma, J., Zhou, C., Zhou, J., & Yang, H. (2022). M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems. arXiv preprint arXiv:2205.08084.	arxiv	2022	N/A	M6
N/A	Shen, T., Li, J., Bouadjenek, M. R., Mai, Z., & Sanner, S. (2023). Towards understanding and mitigating unintended biases in language model-driven conversational recommendation. Information Processing & Management, 60(1), 103139.	Inf Process Manag	2023	Python	BERT
P5	Geng, S., Liu, S., Fu, Z., Ge, Y., & Zhang, Y. (2022, September). Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems (pp. 299-315).	RecSys	2022	Python	T5
PEPLER	Li, L., Zhang, Y., & Chen, L. (2023). Personalized prompt learning for explainable recommendation. ACM Transactions on Information Systems, 41(4), 1-26.	TOIS	2023	Python	GPT-2
N/A	Zhang, Y., Ding, H., Shui, Z., Ma, Y., Zou, J., Deoras, A., & Wang, H. (2021). Language models as recommender systems: Evaluations and limitations.	NeurIPS workshop	2021	N/A	BERT/GPT-2

Related Survey

Paper	Venue	Year
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions	arxiv	2023
Zhang, K., Cao, Q., Sun, F., Wu, Y., Tao, S., Shen, H., & Cheng, X. (2023). Robust Recommender System: A Survey and Future Directions. arXiv preprint arXiv:2309.02057.	arxiv	2023
Li, L., Zhang, Y., Liu, D., & Chen, L. (2023). Large Language Models for Generative Recommendation: A Survey and Visionary Discussions. ArXiv. /abs/2309.01157	arxiv	2023
Chen, X., Li, Z., Pan, W., & Ming, Z. (2023). A Survey on Multi-Behavior Sequential Recommendation. arXiv preprint arXiv:2308.15701.	arxiv	2023
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., ... & Chen, E. (2023). When large language models meet personalization: Perspectives of challenges and opportunities. arXiv preprint arXiv:2307.16376.	arxiv	2023
Fan, W., Zhao, Z., Li, J., Liu, Y., Mei, X., Wang, Y., ... & Li, Q. (2023). Recommender systems in the era of large language models (llms). arXiv preprint arXiv:2307.02046.	arxiv	2023
Li, X., Zhang, Y., & Malthouse, E. C. (2023). A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News. arXiv preprint arXiv:2306.10702.	arxiv	2023
Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Li, X., ... & Zhang, W. (2023). How Can Recommender Systems Benefit from Large Language Models: A Survey. arXiv preprint arXiv:2306.05817.	arxiv	2023
Liu, P., Zhang, L., & Gulla, J. A. (2023). Pre-train, prompt and recommendation: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735.	arxiv	2023

Common Datasets

Name	Scene	Tasks	Information	URL
Amazon Review	Commerce	Seq Rec/CF Rec	This is a large crawl of product reviews from Amazon. Ratings: 82.83 million, Users: 20.98 million, Items: 9.35 million, Timespan: May 1996 - July 2014	link
Amazon-M2	Commerce	Seq Rec/CF Rec	A large dataset of anonymized user sessions with their interacted products collected from multiple language sources at Amazon. It includes 3,606,249 train sessions, 361,659 test sessions, and 1,410,675 products.	link
Steam	Game	Seq Rec/CF Rec	Reviews represent a great opportunity to break down the satisfaction and dissatisfaction factors around games. Reviews: 7,793,069, Users: 2,567,538, Items: 15,474, Bundles: 615	link
MovieLens	Movie	General	The dataset consists of 4 sub-datasets, which describe users' ratings to movies and free-text tagging activities from MovieLens, a movie recommendation service.	link
Yelp	Commerce	General	There are 6,990,280 reviews, 150,346 businesses, 200,100 pictures, 11 metropolitan areas, 908,915 tips by 1,987,897 users. Over 1.2 million business attributes like hours, parking, availability, etc.	link
Douban	Movie, Music, Book	Seq Rec/CF Rec	This dataset includes three domains, i.e., movie, music, and book, and different kinds of raw information, i.e., ratings, reviews, item details, user profiles, tags (labels), and date.	link
MIND	News	General	MIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users. Every news contains textual content including title, abstract, body, category, and entities.	link
U-NEED	Commerce	Conversation Rec	U-NEED consists of 7,698 fine-grained annotated pre-sales dialogues, 333,879 user behaviors, and 332,148 product knowledge tuples.	link
PixelRec	Short Video	Seq Rec/CF Rec	PixelRec is a large dataset of cover images collected from a short video recommender system, comprising approximately 200 million user image interactions, 30 million users, and 400,000 video cover images. The texts and other aggregated attributes of videos are also included.	link

Single card (RTX 3090) debuggable generative language models that support Chinese corpus

Some open-source and effective projects can be adpated to the recommendation systems based on Chinese textual data. Especially for the individual researchers !

Project	Year
baichuan-7B	2023
YuLan-chat	2023
Chinese-LLaMA-Alpaca	2023
THUDM/ChatGLM-6B	2023
FreedomIntelligence/LLMZoo Phoenix	2023
bloomz-7b1	2023
LianjiaTech/BELLE	2023

Hope our conclusion can help your work.