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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The official GitHub page for the survey paper "Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond". And this paper is under review.
| Date | Stars |
|---|---|
| 2026-07-31 | 257 |
| 2026-08-06 | 257 |
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growth rate 0.00%/day
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<h1><b>Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond</b></h1>
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The official GitHub page for the survey paper "Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond".
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<a href="https://arxiv.org/abs/2410.19744" target="_blank"><img src="https://img.shields.io/badge/arXiv-2410.19744-009688.svg" alt="arXiv"></a>
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[arXiv][https://arxiv.org/abs/2410.19744]
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[机器学习与推荐算法][https://mp.weixin.qq.com/s/2xM0Ax1P7ZprG5e6NL3WLA]
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## Basic Information
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<img src="./figs/paper-information.png" alt="Pyramid" width="800" />
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## Introduction
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<img src="./figs/pyramid.png" alt="Pyramid" width="800" />
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### The comparison between this work and existing surveys
| **Paper** | **Non-Gen. RS** | **Gen. RS** | **Scen.** | **Aca.** | **Ind.** | **Pipeline** | **Highlights** |
|---------------------------------------------------------------------------------------------|-------|-----------|-------------------------------------|----------|-----------|------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
| 'A survey on large language models for recommendation' | ✅ | ✅ | common (all kinds) | ✅ | | (1) Discriminative LLM4REC (2) Generative LLM4REC Modeling Paradigms: (i) LLM Embeddings + RS (ii) LLM Tokens + RS (iii) LLM as RS | focuses on expanding the capacity of language models |
| 'How Can Recommender Systems Benefit from Large Language Models: A Survey' | ✅ | | common (all kinds) | | ✅ | (1) Where to adapt to LLM (2) How to adapt to LLM | from the angle of the whole pipeline in industrial recommender systems |
| 'A Survey on Large Language Models for Personalized and Explainable Recommendations' | ✅ | | personalized and explainable RecSys | ✅ | | (1) Explanation Generating for Recommendation | focuses on utilizing LLMs for personalized explanation generating task |
| 'Recommender systems in the era of large language models (llms)' | ✅ | | common (all kinds) | ✅ | | (1) Pre-training (2) Fine-tuning (3) Prompting | comprehensively reviews such domain-specific techniques for adapting LLMs to recommendations |
| 'A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)' | | ✅ | (1) interaction-driven (2) text-driven (3) multimodal | ✅ | | (1) Generative Models for Excerpt of 45,490 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a7567cd48ada52da, llm:Repository is the official GitHub page for a survey paper titled 'Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond' (description).
matched fp:a7567cd48ada52da, llm:Repository is the official GitHub page for a survey paper titled 'Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond' (description).
matched fp:a7567cd48ada52da, llm:Repository is the official GitHub page for a survey paper titled 'Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond' (description).