# CHIANGEL/Awesome-LLM-for-RecSys

Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.

Repository: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys
Canonical: https://ross.abutalabs.com/products/awesome-llm-for-recsys
License: MIT
License Family: permissive
Topics: llm, rs, recsys, large-language-models, recommender-systems, awesome, llm4rec, llm4rs
Last push: 2026-01-17T03:14:41+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 62, release rhythm 35, longevity 85
- inputs: {"age_days": 1200, "days_push": 228, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1550, forks 89 (observed 2026-08-28T04:05:02.148583+00:00)

## What it is
A curated awesome-list of papers and resources on applying large language models to recommender systems, accompanying a survey published in ACM TOIS. It organizes research by where the LLM is adapted in the recommendation pipeline and tracks the newest works separately.

## Use cases
- find papers on LLM-based recommender systems
- survey how LLMs can improve recommendations
- research LLM4Rec topics for a literature review
- keep up with the latest LLM recommender system research
- learn taxonomy of LLM usage in recommendation pipelines
- find baselines for LLM-enhanced recommendation experiments

## When to choose
- starting research on LLMs for recommender systems
- building a related-work section on LLM4Rec
- tracking new papers in this niche

## When to avoid
- you need runnable recommender system code
- you want a production recommendation engine
- you need non-LLM classical RecSys resources only

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

## Member repositories
- CHIANGEL/Awesome-LLM-for-RecSys (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:02.148583+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 2026-08-30T04:30:18.741321+00:00, confidence not recorded.
  - readme: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys (fetched 2026-08-28T04:05:02.148583+00:00, sha 6ddcc85ea679)
- Data as of 2026-08-30T08:39:29.467469+00:00.