Search and recommendations Sections

Search and recommender systems

Finding and ranking information, recommending relevant content and allocating advertising opportunities.

2025 figures. Papers are OpenAlex query matches; author ranges and investment allocations are scenarios. Author counts marked “observed” cover the full query result. Areas overlap and cannot be added together. About the data.

Bars compare publication counts within this page. Topic tags describe subtopics, not separately measured markets. Organization examples link to their sources; activity was checked in September 2026.

Information retrieval and RAG

Sparse and dense retrieval, embeddings, reranking, learning-to-rank, indexing, and retrieval-augmented generation.

RAG Neural search Retrieval-augmented generation
7,700 papers
3.4× vs. 2023
Authors · scenario
5.6k–28k
Investment · scenario
$2.46B–$9.85B
Sources and methodology

Publication activity

2019
1,696
2020
1,927
2021
1,874
2022
1,888
2023
2,265
2024
4,234
2025
7,700

Matches include papers applying these methods. Search precision and recall have not been systematically measured.

Title and abstract matches for articles, preprints and reviews; retracted work is excluded. Versions and overlapping areas may be counted more than once.

("information retrieval" OR "learning to rank" OR "dense retrieval" OR "neural retrieval" OR "retrieval augmented generation" OR "retrieval augmented language model")

Retrieved 2026-09-26.

OpenAlex query results

Publishing authors

Base scenario: 9.4k publishing authors. Paper count × 3.64 known authors per paper ÷ 3 papers per author per year. The range assumes 1–5 papers per author; the base and both ends of the range are at least the 3,564 distinct Author IDs already observed in the sample.

The publication rate is an assumption, not a measured rate for this field. The range is a scenario, not a confidence interval.

Random sample: 1,000 of 7,700 papers (13.0%). 12.1% of authorship records lack an Author ID; 9.9% of retrieved works lack an abstract. Retrieved 2026-09-26.

Mean known authors per paper: 3.64; 95% bootstrap interval 3.45–3.87. This describes sampling variability, not search accuracy or uncertainty in the number of researchers.

Applied coauthors are included. Missing or incorrectly linked author records affect these figures. They do not measure research jobs or everyone working in a field.

OpenAlex sample query

Private investment

An assumed allocation of broader investment segments. The range is 0.5–2× the base case. Shares are editorial assumptions, not measured deals or research spending.

Base case: $4.92B.

  • Data management, processing: $31.6B × 8.00%
  • Marketing, digital ads: $2.51B × 10.00%
  • Retail: $4.10B × 20.00%
  • Legal tech: $3.79B × 35.00%

Company investment, not revenue or research spending. Ranges are scenarios, not confidence intervals.

Stanford AI Index 2026 / Quid

Scope and related research

RAG is one part of the broader information-retrieval field.

Organizations

Selected examples
  • Cohere Models

    Embed and Rerank models support semantic search and relevance ranking.

  • Pinecone Tools & infrastructure

    Vector search.

  • Weaviate Tools & infrastructure

    Vector database.

More organizations (12)
  • Algolia Product

    Search and recommendations.

  • AlphaSense Product

    Financial and market research.

  • Coveo Product

    Enterprise search and recommendations.

  • Elastic Tools & infrastructure

    Search and retrieval infrastructure.

  • Glean Product

    Enterprise search and agents.

  • Perplexity Tools & infrastructure

    Search and embedding APIs provide web retrieval, ranking and semantic search.

  • Qdrant Tools & infrastructure

    Vector database.

  • Sana Product

    AI learning and knowledge.

    Part of Workday
  • Sourcegraph Product

    Code intelligence.

  • TwelveLabs Models

    Marengo provides multimodal embeddings and semantic search over video moments.

  • Vespa Tools & infrastructure

    Search and recommendation serving.

  • Zilliz Tools & infrastructure

    Vector database and Milvus.

Recommender systems

Candidate retrieval, sequential and generative recommendation, and user-interest modeling.

Recommendation Personalization
7,465 papers
1.6× vs. 2023
Authors · scenario
4.5k–22k
Investment · scenario
$1.46B–$5.84B
Sources and methodology

Publication activity

2019
2,637
2020
3,119
2021
3,493
2022
3,876
2023
4,603
2024
5,826
2025
7,465

Matches include papers applying these methods. Search precision and recall have not been systematically measured.

Title and abstract matches for articles, preprints and reviews; retracted work is excluded. Versions and overlapping areas may be counted more than once.

("recommender system" OR "recommendation system" OR "sequential recommendation" OR "generative recommendation" OR "collaborative filtering")

Retrieved 2026-09-26.

OpenAlex query results

Publishing authors

Base scenario: 7.5k publishing authors. Paper count × 3.01 known authors per paper ÷ 3 papers per author per year. The range assumes 1–5 papers per author; the base and both ends of the range are at least the 2,912 distinct Author IDs already observed in the sample.

The publication rate is an assumption, not a measured rate for this field. The range is a scenario, not a confidence interval.

Random sample: 1,000 of 7,465 papers (13.4%). 14.9% of authorship records lack an Author ID; 16.1% of retrieved works lack an abstract. Retrieved 2026-09-26.

Mean known authors per paper: 3.01; 95% bootstrap interval 2.87–3.15. This describes sampling variability, not search accuracy or uncertainty in the number of researchers.

Applied coauthors are included. Missing or incorrectly linked author records affect these figures. They do not measure research jobs or everyone working in a field.

OpenAlex sample query

Private investment

An assumed allocation of broader investment segments. The range is 0.5–2× the base case. Shares are editorial assumptions, not measured deals or research spending.

Base case: $2.92B.

  • Marketing, digital ads: $2.51B × 20.00%
  • Retail: $4.10B × 50.00%
  • Entertainment: $3.66B × 10.00%

Company investment, not revenue or research spending. Ranges are scenarios, not confidence intervals.

Stanford AI Index 2026 / Quid

Scope and related research

Advertising auctions and bidding are listed separately, although the methods overlap.

Organizations

Selected examples
  • Netflix Research

    Research on personalized video ranking and recommendation-page composition.

  • Spotify Research

    Research on music recommendations that balance relevance, exploration and diversity.

  • Meta Research

    Andromeda retrieves personalized recommendations for advertising.

More organizations (3)
  • Coveo Product

    Enterprise search and recommendations.

  • Recombee Product

    Recommendation systems.

  • Vespa Tools & infrastructure

    Search and recommendation serving.

Ads ranking and auction design

Click and conversion prediction, attribution, bidding, auctions, and ad-delivery optimization.

AdTech Bidding Click prediction
160 papers
1.4× vs. 2023
Authors · observed
443
Investment · scenario
$816M–$3.26B
Sources and methodology

Publication activity

2019
63
2020
102
2021
93
2022
132
2023
116
2024
129
2025
160

Matches include papers applying these methods. Search precision and recall have not been systematically measured.

Title and abstract matches for articles, preprints and reviews; retracted work is excluded. Versions and overlapping areas may be counted more than once.

("click through rate prediction" OR "conversion rate prediction" OR "computational advertising" OR "real time bidding" OR "ad auction" OR "advertising attribution")

Retrieved 2026-09-26.

OpenAlex query results

Publishing authors

443 distinct OpenAlex Author IDs across all 160 matching papers. This is an observed count within the query result; no publication-rate assumption is used.

Full query result: 160 of 160 papers (100.0%). 6.8% of authorship records lack an Author ID; 21.2% of retrieved works lack an abstract. Retrieved 2026-09-26.

Applied coauthors are included. Missing or incorrectly linked author records affect these figures. They do not measure research jobs or everyone working in a field.

OpenAlex sample query

Private investment

An assumed allocation of broader investment segments. The range is 0.5–2× the base case. Shares are editorial assumptions, not measured deals or research spending.

Base case: $1.63B.

  • Marketing, digital ads: $2.51B × 65.00%

Company investment, not revenue or research spending. Ranges are scenarios, not confidence intervals.

Stanford AI Index 2026 / Quid

Scope and related research

Draws on ranking, causal inference, and mechanism design.

Organizations

Selected examples
  • Google Ads Product

    Performance Max applies Google AI to bidding and ad delivery.

  • Meta Research

    Andromeda applies machine learning to personalized ad retrieval.

  • Moloco Product

    ML for advertising and commerce.

More organizations (3)