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Recommender Systems and Techniques
TopicLeading institutions, researchers & key papers
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.
72
Works
IDs:OpenAlex
How has Recommender Systems and Techniques's publication output changed over time?
ScholarIQpublication output · 2005–2023
Output grew0% over the shown period — from 1 works in 2005 to 1 in 2023.
1
1
1
1
1
4
2
2
1
1
2005200720122016201720192020202120222023
What are the most-cited papers on Recommender Systems and Techniques?
ScholarIQmost cited works
Graph Contextualized Self-Attention Network for Session-based Recommendation
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou
2019634 CitationsOPEN ACCESS
Are Graph Augmentations Necessary?
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen
S4363608773. 2022605 CitationsOPEN ACCESS
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Quoc Viet Hung Nguyen, Xiangliang Zhang
2021498 CitationsOPEN ACCESS
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction
Sungyong Seo, Jing Huang, Hao Yang, Yan Liu
2017465 Citations
Where is Recommender Systems and Techniques research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
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How much of the research on Recommender Systems and Techniques is open access?
ScholarIQopen access share
53%OPEN ACCESS
Gold
33%
Green
20%
Hybrid
0%
Bronze
0%
Closed
47%
Related on ScholarIQ
Accurately interpreting clickthrough data as implicit feedback
Paper
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
Paper
Graph Contextualized Self-Attention Network for Session-based Recommendation
Paper
Are Graph Augmentations Necessary?
Paper
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Paper
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction
Paper