wlik / llm4rec-awesome-papers Goto Github PK

Hi @WLiK,

😊 Thanks so much for sharing such a nice repo on LLMs for Rec! I am wondering if our recent paper "Representation Learning with Large Language Models for Recommendation" could be included in this list?

paper link: https://arxiv.org/abs/2310.15950
code link: https://github.com/HKUDS/RLMRec

Based on your toxonomy, it is No Tuning + Python code + GPT-3.5-turbo.

I would appreciate it if you consider attaching this paper the code link to your awesome reposity!

Best regards,
Xubin

Just curious because I saw GPT4Rec's entry but didn't see one for BERT4Rec when there are other papers that use BERT. Maybe there could be a distinction as to what qualifies as a "LLM?"

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

The URL for paper LLaRA error changed to E4SRec's in README. The real URL is https://arxiv.org/abs/2312.02445 .

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

These papers represent our team's efforts in the field and align closely with the themes of your project. We believe they could provide valuable insights and support to your work.

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 @WLiK,

Thank you for maintaining this repo of LLM4Rec, we recently have a workshop paper call LlamaRec for LLM-based two-stage recommendation. The paper can be found at https://arxiv.org/abs/2311.02089 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!

Hi @WLiK,

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!

Dear Repo Owners,

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 (in our proposed Task 3). Would you mind adding this work to your repo?

I tried to pull a request for adding this paper but I am not sure which category it belongs to (it may fit better into a dataset category). Indeed, we also tested some baseline models like mT5 by fine-tuning them. So I guess it may also fit into the fine-tuning category.

Thank you for your attention.