# hongleizhang/RSPapers

RSTutorials: A Curated List of Must-read Papers on Recommender System.

Repository: https://github.com/hongleizhang/RSPapers
Canonical: https://ross.abutalabs.com/products/rspapers
License: MIT
License Family: permissive
Topics: read-papers, recommender-system, social-recommendation, survey, deep-learning-based-rs, social-recommender, collaborative-filtering, deep-learning
Last push: 2026-03-12T13:22:40+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 71, release rhythm 35, longevity 100
- inputs: {"age_days": 3137, "days_push": 174, "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 6511, forks 1344 (observed 2026-08-28T04:09:44.814439+00:00)

## What it is
A curated list of must-read papers, surveys, and tutorials on recommender systems, covering topics from collaborative filtering to LLM-based and agentic recommenders. It is a reading resource rather than executable software.

## Use cases
- find must-read papers on recommender systems
- learn about deep learning based recommendation
- survey literature on social recommendation
- study LLM and agentic recommender systems
- prepare for research in collaborative filtering

## When to choose
- you need an organized reading list for recommender system research
- you want surveys and tutorials across many RS subtopics

## When to avoid
- you need runnable recommendation algorithms or code
- you need a dataset rather than paper references

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: machine-learning, artificial-intelligence, tutorials, awesome-lists
- platform: cross-platform
- tags: recommender-system, papers, curated-list, collaborative-filtering, deep-learning, llm, survey

## Member repositories
- hongleizhang/RSPapers (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:44.814439+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 2026-08-29T17:44:26.918205+00:00, confidence not recorded.
  - readme: https://github.com/hongleizhang/RSPapers (fetched 2026-08-28T04:09:44.814439+00:00, sha 07b5f5894ff0)
- Data as of 2026-08-30T08:39:29.467469+00:00.