# WLiK/LLM4Rec-Awesome-Papers

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

Repository: https://github.com/WLiK/LLM4Rec-Awesome-Papers
Canonical: https://ross.abutalabs.com/products/llm4rec-awesome-papers
License Family: other
Topics: large-language-models, recommender-system, survey, llm4rec, datasets, awesome
Last push: 2025-03-17T11:31:02+00:00

## Health v2 (maintenance only)
Score: 34/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 11, release rhythm 35, longevity 86
- inputs: {"age_days": 1210, "days_push": 534, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2309, forks 164 (observed 2026-08-28T04:06:35.947920+00:00)

## What it is
A curated awesome-list of papers, surveys, tutorials, datasets, and code resources on applying large language models to recommender systems. It accompanies the published survey 'A Survey on Large Language Models for Recommendation' and is continuously updated.

## Use cases
- find papers on LLM-based recommendation systems
- research how large language models are used for recommendations
- find datasets for LLM recommender system experiments
- keep up with new LLM4Rec research
- find code implementations of LLM recommendation papers
- prepare a literature review on LLMs for recommendation

## When to choose
- you are researching the intersection of LLMs and recommender systems
- you need a curated starting point of papers and datasets on LLM4Rec
- you want to track recent academic work in this area

## When to avoid
- you need production-ready recommendation software rather than research references
- you want a maintained code library instead of a paper list

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: large-language-models, artificial-intelligence, awesome-lists, tutorials
- platform: -
- tags: awesome-list, recommender-system, survey, papers, llm4rec, datasets, research, web-server

## Member repositories
- WLiK/LLM4Rec-Awesome-Papers (main) score 34

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:35.947920+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:39:34.758366+00:00, confidence not recorded.
  - readme: https://github.com/WLiK/LLM4Rec-Awesome-Papers (fetched 2026-08-28T04:06:35.947920+00:00, sha 46856c347e16)
- Data as of 2026-08-30T08:39:29.467469+00:00.