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. 9.8% 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
In the world top 10%: 2.4k
Top-10% rate: 9.8%
Growth, 2018-21 → 2022-25: +47%
Largest producer: China

Recommender Systems and Techniques

Which countries lead Recommender Systems and Techniques research?

By volume, China and India publish the most (4k and 979 works in 2022-now).

By volume, 2022-now

1 China 4k works
2 India 979 works
3 United States 765 works
4 Japan 191 works
5 South Korea 186 works
6 Australia 179 works
7 Indonesia 170 works
8 United Kingdom 138 works
9 Italy 128 works
10 ?? 113 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.

China4,011 · 58.5%India979 · 14.3%United States765 · 11.2%Japan191 · 2.8%6 others listed914 · 13.3%

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

Which institutions lead Recommender Systems and Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Recommender Systems and Techniques research, followed by Tsinghua University and University of Science and Technology of China.

By volume, 2022-now

1 Chinese Academy of SciencesChina 92 works
2 Tsinghua UniversityChina 74 works
3 University of Science and Technology of ChinaChina 67 works
4 Shanghai Jiao Tong UniversityChina 56 works
5 University of Electronic Science and Technology of ChinaChina 54 works
6 Alibaba Group (China)China 54 works
7 Renmin University of ChinaChina 53 works
8 Beijing University of Posts and TelecommunicationsChina 51 works
9 Zhejiang UniversityChina 48 works
10 Tencent (China)China 45 works

Who are the leading researchers in Recommender Systems and Techniques?

The most-cited researchers publishing on Recommender Systems and Techniques include Yoshua Bengio, Kai You Wang and Lei Jiang.

1 Yoshua Bengio Canada 476k citations
2 Kai You Wang China 224k citations
3 Lei Jiang China 214k citations
4 Michael I. Jordan United States 182k citations
5 H. Vincent Poor United States 165k citations

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

What are the most cited papers in Recommender Systems and Techniques?

The most cited paper in Recommender Systems and Techniques is "Matrix Factorization Techniques for Recommender Systems" (Yehuda Koren, Robert Bell, Chris T. Volinsky, 2009), with 11,958 citations. Next come "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions" (10,402) and "Item-based collaborative filtering recommendation algorithms" (9,115).

Most cited papers
#	Paper	Released	Citations
1	Matrix Factorization Techniques for Recommender Systems Yehuda Koren, Robert Bell, Chris T. Volinsky · Computer	Aug 2009	11,958
2	Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions Gediminas Adomavičius, Alexander Tuzhilin · IEEE Transactions on Knowledge and Data Engineering	25 Apr 2005	10,402
3	Item-based collaborative filtering recommendation algorithms Badrul M. Sarwar, George Karypis, Joseph A. Konstan et al. Conference paper	Apr 2001	9,115
4	Neural Collaborative Filtering Xiangnan He, Lizi Liao, Hanwang Zhang et al. Conference paperOpen access	3 Apr 2017	6,831
5	Evaluating collaborative filtering recommender systems Jonathan L. Herlocker, Joseph A. Konstan, Loren Terveen et al. · ACM Transactions on Information Systems	2004	5,828

All 98 most cited papers, with filters and sorting

Where is Recommender Systems and Techniques research done?

The largest centres of Recommender Systems and Techniques research in 2022-now are Beijing (China), Shanghai (China), Hangzhou (China) and Wuhan (China). Among places with at least 20 works in it, it is an unusually large share of all research in Beijing.

Largest cities, 2022-now

1 BeijingChina 716 works
2 ShanghaiChina 250 works
3 HangzhouChina 205 works
4 WuhanChina 173 works
5 GuangzhouChina 161 works
6 ShenzhenChina 160 works
7 NanjingChina 153 works
8 ChongqingChina 151 works
9 HefeiChina 143 works
10 ChengduChina 131 works

Where it is the local speciality

Beijing26.1 works7.9×

← less than its size predicts1×more →

Location quotient: how much more of its research is in Recommender Systems and Techniques than the world average.

See Recommender Systems and Techniques on the map

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

Among universities, judged by research, University of Hong Kong, University of Science and Technology of China and City University of Hong Kong 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.

above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged

40%

Specialisationhow much of its work is in this field, counted up to 10× the world share

10%

Sizeits output in this field, on a log scale

30%

Growthoutput growth, counted from -20% to +20%

10%

Internationalshare of its work with a co-author abroad

10%

Each slider sets a weight; the share it gets of the score is beside it.

#	University	Score	Top 10%	Specialisation	Works	Growth
1	University of Hong KongChina	81.0	41.6%	3.7×	26	+406.9%
2	University of Science and Technology of ChinaChina	75.8	23.6%	7.7×	67	+135.6%
3	City University of Hong KongChina	73.2	29.1%	6.1×	26	+219.9%
4	National University of SingaporeSingapore	70.8	27.0%	3.2×	28	+65.1%
5	Hong Kong Polytechnic UniversityChina	70.6	26.1%	4.3×	27	+231.7%
6	Hong Kong University of Science and TechnologyChina	70.3	24.5%	7.2×	22	+43.8%
7	Nanyang Technological UniversitySingapore	69.7	27.1%	4.7×	28	+38.6%
8	Renmin University of ChinaChina	68.7	18.7%	22.0×	53	+162.6%
9	Tsinghua UniversityChina	64.8	20.5%	4.3×	74	-3.3%
10	Zhejiang UniversityChina	64.2	22.7%	2.4×	48	+107.6%
11	University of Technology SydneyAustralia	62.6	24.8%	8.0×	26	0.0%
12	The University of QueenslandAustralia	62.5	31.2%	2.3×	16	+8.8%
13	Hefei University of TechnologyChina	62.3	25.1%	7.3×	30	+35.2%
14	University of Electronic Science and Technology of ChinaChina	61.5	14.6%	6.5×	54	+34.1%
15	Wuhan UniversityChina	59.1	20.2%	3.8×	40	+23.8%
16	Shanghai Jiao Tong UniversityChina	58.7	13.9%	2.6×	56	+46.1%
17	Shandong UniversityChina	57.3	21.5%	2.9×	28	+75.1%
18	Chinese University of Hong KongChina	57.3	20.3%	2.7×	15	+76.4%
19	Chongqing UniversityChina	56.5	16.2%	4.8×	39	+71.2%
20	Griffith UniversityAustralia	56.3	28.1%	3.9×	12	+177.7%
21	Shenzhen UniversityChina	55.5	14.7%	5.8×	32	+42.1%
22	Peking UniversityChina	54.6	15.2%	2.7×	40	+8.5%
23	Hong Kong Baptist UniversityChina	54.4	14.1%	10.8×	15	+117.5%
24	Beihang UniversityChina	53.8	14.5%	4.6×	38	+104.0%
25	Huazhong University of Science and TechnologyChina	52.6	14.8%	2.8×	38	+113.3%
26	Macquarie UniversityAustralia	52.6	13.6%	7.2×	20	+47.1%
27	National University of Defense TechnologyChina	52.4	14.1%	6.4×	37	+54.8%
28	Beijing Jiaotong UniversityChina	51.9	12.5%	7.6×	34	+88.0%
29	Tianjin UniversityChina	51.1	11.4%	4.0×	36	+98.4%
30	Xidian UniversityChina	51.0	10.3%	5.6×	30	+35.8%
31	University of California San DiegoUnited States	50.9	21.6%	1.9×	14	+220.7%
32	Northeastern UniversityChina	50.6	14.5%	4.8×	32	+20.3%
33	University of Illinois Urbana-ChampaignUnited States	50.5	17.1%	3.0×	19	+49.7%
34	Chongqing University of Posts and TelecommunicationsChina	50.3	11.9%	14.8×	31	+37.8%
35	Anhui UniversityChina	50.3	12.8%	8.3×	28	+156.9%
36	Central China Normal UniversityChina	50.3	17.0%	10.1×	19	+105.6%
37	University of KlagenfurtAustria	50.1	21.5%	22.5×	12	+12.5%
38	Polytechnic University of BariItaly	49.6	17.9%	13.1×	12	+30.2%
39	Sun Yat-sen UniversityChina	49.4	12.4%	2.3×	36	+23.2%
40	Jilin UniversityChina	49.3	18.8%	2.8×	24	+60.3%
41	Singapore Management UniversitySingapore	48.9	13.4%	16.0×	13	+18.6%
42	Beijing University of Posts and TelecommunicationsChina	48.3	13.4%	10.8×	51	-19.4%
43	RMIT UniversityAustralia	46.6	13.9%	4.1×	12	+20.0%
44	SRM Institute of Science and TechnologyIndia	45.3	3.7%	5.4×	41	+219.7%
45	Chongqing University of TechnologyChina	44.9	10.4%	14.1×	22	+623.8%
46	Zhejiang University of Science and TechnologyChina	44.7	14.3%	10.1×	14	+30.4%
47	Telkom UniversityIndonesia	42.6	0.0%	10.9×	42	+527.6%
48	City University of MacauChina	41.3	8.7%	8.4×	10	+461.4%
49	Rutgers, The State University of New JerseyUnited States	39.5	30.1%	1.9×	11	-38.3%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Recommender Systems and Techniques, country by country

Is Recommender Systems and Techniques research growing?

Output in 2022-2025 was 47% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Recommender Systems and Techniques.

grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Recommender Systems and Techniques as a percentage of every work published that year, 0.035% in 2024.

Fractional works of Recommender Systems and Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

Questions people ask

Who leads recommender systems research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Recommender Systems and Techniques research, followed by Tsinghua University and University of Science and Technology of China. By volume, China and India publish the most (4k and 979 works in 2022-now).

All 1,273 questions in the Question Catalogue

Discussion

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Which countries lead Recommender Systems and Techniques research?

By volume, 2022-now

How concentrated that is

Which institutions lead Recommender Systems and Techniques research?

By volume, 2022-now

Who are the leading researchers in Recommender Systems and Techniques?

What are the most cited papers in Recommender Systems and Techniques?

Where is Recommender Systems and Techniques research done?

Largest cities, 2022-now

Where it is the local speciality

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

Is Recommender Systems and Techniques research growing?

Its share of all research

How much of it reaches the world top 10%

Which topics inside it are moving

Related research

Questions people ask

Who leads recommender systems research?

Discussion