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Topic · Information Systems

Recommender Systems and Techniques

Recommender Systems and Techniques is a research topic within Information Systems. Science Explorer counts 25k research works in it since 1962. 33.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the advancements in recommender system technologies, including collaborative filtering, matrix factorization, deep learning, content-based recommendation, web mining, context-aware recommender systems, neural networks, user modeling, and trust-aware recommender systems. The papers cover various techniques and methodologies for improving recommendation accuracy and addressing challenges such as cold start problems and privacy concerns.

  • Collaborative Filtering
  • Matrix Factorization
  • Deep Learning
  • Content-Based Recommendation
  • Web Mining
  • Context-Aware Recommender Systems
  • Neural Networks
  • User Modeling
  • Trust-Aware Recommender Systems
  • Click-Through Rate Prediction
Research works
25k
fractional, since 1962
In the world top 10%
8.3k
per year above
Top-10% rate
33.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+43%
the tick is no change

Which countries lead Recommender Systems and Techniques research?

By volume, China and India publish the most (3.4k and 793 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 3.4k works
  2. 2 India 793 works
  3. 3 United States 660 works
  4. 4 Japan 176 works
  5. 5 South Korea 163 works
  6. 6 Australia 162 works
  7. 7 Indonesia 149 works
  8. 8 Hong Kong 123 works
  9. 9 United Kingdom 119 works
  10. 10 Italy 117 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 58.3%India: 13.4%United States: 11.2%Japan: 3.0%6 others listed: 14.1%58%largest
China3,447 · 58.3%India793 · 13.4%United States660 · 11.2%Japan176 · 3.0%6 others listed832 · 14.1%

Shares of the rows listed above, not of the whole node.

Which institutions lead Recommender Systems and Techniques research?

By volume in 2022–2025, Tsinghua University publishes the most Recommender Systems and Techniques research, followed by University of Science and Technology of China and Shanghai Jiao Tong University.

By volume, 2022–2025

  1. 1 Tsinghua UniversityChina 68 works
  2. 2 University of Science and Technology of ChinaChina 61 works
  3. 3 Shanghai Jiao Tong UniversityChina 50 works
  4. 4 University of Electronic Science and Technology of ChinaChina 50 works
  5. 5 Renmin University of ChinaChina 48 works
  6. 6 Beijing University of Posts and TelecommunicationsChina 45 works
  7. 7 Alibaba Group (China)China 45 works
  8. 8 Zhejiang UniversityChina 42 works
  9. 9 Telkom UniversityIndonesia 40 works
  10. 10 Tencent (China)China 40 works

Who are the leading researchers in Recommender Systems and Techniques?

The most-cited researchers publishing on Recommender Systems and Techniques include Philip S. Yu, Jiawei Han and Susan Dumais.

  1. 1 Philip S. Yu United States 6.6k citations
  2. 2 Jiawei Han United States 5.2k citations
  3. 3 Susan Dumais United States 4.8k citations
  4. 4 Zhu Han United States 4.7k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Recommender Systems and Techniques research done?

The largest centres of Recommender Systems and Techniques research in 2022–2025 are Beijing (China), Shanghai (China), Hangzhou (China) and Wuhan (China).

Largest cities, 2022–2025

  1. 1 Beijing China 648 works
  2. 2 Shanghai China 226 works
  3. 3 Hangzhou China 181 works
  4. 4 Wuhan China 150 works
  5. 5 Shenzhen China 145 works
  6. 6 Guangzhou China 144 works
  7. 7 Chongqing China 137 works
  8. 8 Nanjing China 137 works
  9. 9 Hefei China 127 works
  10. 10 Chengdu China 117 works
See Recommender Systems and Techniques on the map

Where is the best place to study Recommender Systems and Techniques?

Among universities, judged by research, Renmin University of China, Polytechnic University of Bari and Hong Kong Polytechnic University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%50%100%mean 53.48%fractional works in this node (log) →share in the world top 10% →Renmin University of China: 48, 54.8%Polytechnic University of Bari: 12, 59.5%Hong Kong Polytechnic University: 24, 58.8%University of Technology Sydney: 24, 55.6%Chongqing University of Posts and Telecommunications: 30, 27.9%Hefei University of Technology: 24, 62.6%University of Science and Technology of China: 61, 55.6%Beijing University of Posts and Telecommunications: 45, 34.3%The University of Queensland: 15, 73.8%Tsinghua University: 68, 51.9%Polytechnic Universi…Hong Kong Polytechni…University of Techno…Renmin University of…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Renmin University of ChinaChina 71.554.8%20.2×48 +41.8%
2 Polytechnic University of BariItaly 70.659.5%13.1×12 +201.7%
3 Hong Kong Polytechnic UniversityHong Kong 66.058.8%3.8×24 +235.9%
4 University of Technology SydneyAustralia 65.755.6%7.2×24 +107.9%
5 Chongqing University of Posts and TelecommunicationsChina 63.727.9%14.1×30 +196.1%
6 Hefei University of TechnologyChina 63.762.6%6.0×24 +165.3%
7 University of Science and Technology of ChinaChina 63.355.6%7.0×61 +10.1%
8 Beijing University of Posts and TelecommunicationsChina 61.234.3%10.0×45 +46.6%
9 The University of QueenslandAustralia 60.273.8%2.5×15 +369.5%
10 Tsinghua UniversityChina 60.151.9%4.7×68 +64.5%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Recommender Systems and Techniques research growing?

Output in 2018–2022 was 43% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Recommender Systems and Techniques.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.