archersama / awesome-recommend-system-pretraining-papers Goto Github PK

Hi @archersama,

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, under the section "Large Language Models for Recommendation"? Thank you!

Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems, arxiv 2022, [paper]

paper url is incorrect. maybe the correct url is https://arxiv.org/abs/2107.13586

Hi Xiangyang,

Thanks for your awesome repository! I believe it will help many researchers gain insights into the latest PLM-based techniques and benefit the RS community.

I am happy to announce our paper, "MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation" (https://arxiv.org/abs/2308.11175), on ACM MM 2023. This paper is about integrating multi-modal information (using the pre-trained CLIP model) into behavior sequence representation to support universal recommendation. The data and code are also available at https://github.com/gimpong/MM23-MISSRec.

If the paper falls within the scope of this repository, I would appreciate it if you considered including it in the paper list. :)

Kind regards,
Jinpeng

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

Hello! Our new work titled ‘Uncovering ChatGPT’s Capabilities in Recommender Systems’ has been released and can be found at https://arxiv.org/pdf/2305.02182.pdf. We have also open-sourced our code and detailed results at https://github.com/rainym00d/LLM4RS. We kindly request that please add it to this repository. Thank you!

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.