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 China 3.4k works
- 2 India 793 works
- 3 United States 660 works
- 4 Japan 176 works
- 5 South Korea 163 works
- 6 Australia 162 works
- 7 Indonesia 149 works
- 8 Hong Kong 123 works
- 9 United Kingdom 119 works
- 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.
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 Tsinghua University China 68 works
- 2 University of Science and Technology of China China 61 works
- 3 Shanghai Jiao Tong University China 50 works
- 4 University of Electronic Science and Technology of China China 50 works
- 5 Renmin University of China China 48 works
- 6 Beijing University of Posts and Telecommunications China 45 works
- 7 Alibaba Group (China) China 45 works
- 8 Zhejiang University China 42 works
- 9 Telkom University Indonesia 40 works
- 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 Philip S. Yu 6.6k citations
- 2 Jiawei Han 5.2k citations
- 3 Susan Dumais 4.8k citations
- 4 Zhu Han 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).
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.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Renmin University of China China | 71.5 | 54.8% | 20.2× | 48 | +41.8% |
| 2 | Polytechnic University of Bari Italy | 70.6 | 59.5% | 13.1× | 12 | +201.7% |
| 3 | Hong Kong Polytechnic University Hong Kong | 66.0 | 58.8% | 3.8× | 24 | +235.9% |
| 4 | University of Technology Sydney Australia | 65.7 | 55.6% | 7.2× | 24 | +107.9% |
| 5 | Chongqing University of Posts and Telecommunications China | 63.7 | 27.9% | 14.1× | 30 | +196.1% |
| 6 | Hefei University of Technology China | 63.7 | 62.6% | 6.0× | 24 | +165.3% |
| 7 | University of Science and Technology of China China | 63.3 | 55.6% | 7.0× | 61 | +10.1% |
| 8 | Beijing University of Posts and Telecommunications China | 61.2 | 34.3% | 10.0× | 45 | +46.6% |
| 9 | The University of Queensland Australia | 60.2 | 73.8% | 2.5× | 15 | +369.5% |
| 10 | Tsinghua University China | 60.1 | 51.9% | 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.
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.