# guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Pre-Ranking, Ranking, Post Ranking, Relevance, LLM and RL. Please cite our paper "Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives" (TOIS 2026).

Repository: https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising
Canonical: https://ross.abutalabs.com/products/awesome-deep-learning-papers-for-search-recommendation-advertising
Language: Python
License Family: other
Topics: deep-learning, recommender-system, search, advertising, ctr, cvr, reinforcement-learning, search-engine
Last push: 2026-04-25T11:36:26+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 79, release rhythm 35, longevity 100
- inputs: {"age_days": 2187, "days_push": 130, "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 2586, forks 296 (observed 2026-08-28T04:07:02.679364+00:00)

## What it is
A curated awesome-list of deep learning papers for industrial search, recommendation, and online advertising systems, organized by pipeline stages such as embedding, matching, pre-ranking, ranking, and post-ranking. It accompanies a TOIS 2026 survey paper on deep learning to rank.

## Use cases
- find papers on CTR prediction models
- learn about embedding and matching techniques for recommender systems
- research deep learning to rank in search engines
- study LLM-based ranking approaches
- find reinforcement learning papers for advertising
- survey industrial recommendation system literature

## When to choose
- you need a curated reading list for search, recommendation, or ads research
- you want papers organized by ranking pipeline stage
- you are writing a survey or literature review on learning to rank

## When to avoid
- you need runnable code or a library rather than paper references
- you want tutorials for beginners rather than research papers

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, search-engine, nlp, reinforcement-learning
- domain: machine-learning, deep-learning, tutorials, awesome-lists
- platform: python
- tags: awesome-list, recommender-systems, ctr-prediction, cvr-prediction, learning-to-rank, advertising, academic-papers, llm-ranking, search

## Member repositories
- guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:02.679364+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 2026-08-30T02:22:26.354445+00:00, confidence not recorded.
  - readme: https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising (fetched 2026-08-28T04:07:02.679364+00:00, sha 5ab23e159e7d)
- Data as of 2026-08-30T08:39:29.467469+00:00.