nancheng58 / awesome-llm4rs-papers Goto Github PK

Dear Repo Owner,

Thank you for maintaining this nice repo of LLM for Recommendation. We have recently released a session-recommendation dataset named Amazon-M2 for evaluating LLMs in recommendation scenarios. It provides both a dataset, a benchmark, and three text-related tasks. Would you mind adding this work to your repo?

Thank you for your attention.

Knowledge Prompt-tuning for Sequential Recommendation
code

Hi,

There is a new paper that discusses leveraging LLMs to obtain better explanations iteratively, and It then explores using enriched explanations to enhance Visualization Recommendations.

LLM4Vis: Explainable Visualization Recommendation using ChatGPT
Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim and Yong Wang
EMNLP Industry 2023 | paper | code

Hi @nancheng58,

Thanks so much for sharing such a nice repo on LLMs for Rec! I am wondering if our recent paper Large Language Models as Zero-Shot Conversational Recommenders could be included in this list?

Our contributions include (1) datasets: new conversational recommendation datasets from Reddit; (2) LLMs evaluation on conversational recommendation tasks and related analysis.

Thanks in advance!

Main issue is as the title said..

Hi, we recently came across your project and were impressed by its scope and objectives. We believe that our research papers could be a valuable addition to your project as references:

1 NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Link: https://arxiv.org/pdf/2309.07705.pdf
2 A Content-Driven Micro-Video Recommendation Dataset at Scale
Link: https://arxiv.org/pdf/2309.15379.pdf

We kindly request you to consider including our papers in your project. If you require any additional information or have any questions, please do not hesitate to contact us.

Hi @nancheng58,

Thank you for maintaining this great repo! We had a paper published in 2021 titled "Language models as recommender systems: Evaluations and limitations". This is the first work leveraging pre-trained LLMs for recommendation using prompting methods. Would you like to add this work to your repo?

Hi @nancheng58,

Thank you for maintaining this repo of LLM Recsys, we recently release a workshop paper call LlamaRec for LLM-based sequential recommender. The paper can be found at https://github.com/Yueeeeeeee/LlamaRec/blob/main/media/paper.pdf (ArXiv will be available soon) and the code can be found at: https://github.com/Yueeeeeeee/LlamaRec

Would you mind adding this work to your repo as well? Thank you!

Dear Owner,

Thank you for maintaining this great repo!
Would you like to add our paper to your repo?

RDRec: Rationale Distillation for LLM-based Recommendation, ACL 2024 Main (short).
ArXiv: https://arxiv.org/pdf/2405.10587
Code: https://github.com/WangXFng/RDRec

Thank you very much!

Please consider including our paper in your repository. Thank you very much!

Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
https://arxiv.org/abs/2405.03988

Hi! Thank you for creating and maintaining such a great repository! There is a new paper that reveals the security vulnerabilities of LLM-based RS due to their emphasis on the textual content of items:

Stealthy Attack on Large Language Model based Recommendation
Link: https://arxiv.org/abs/2402.14836

We kindly request you to consider including our paper in your repository. Thank you very much!

Hi,
I have created this new PR, for adding one of the most recent survey paper on LLMs in Recommender System, The paper added in this PR was arxived in 2024, so it has all the recent work done in this area so far. I will like this paper to be added in your github list under survey paper. Thanks

Dear Authors,

Thank you for your valuable contributions to the recommender system community! I wanted to ask if you could kindly add another paper to your GitHub repository. The paper, "A-LLMRec: Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System", has been accepted at KDD 2024. It explores leveraging collaborative knowledge from a pre-trained collaborative filtering recommender system to LLMs. You can find the paper at this link: https://arxiv.org/abs/2404.11343.

Thank you in advance!