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Recommender Systems and Techniques

TopicLeading institutions, researchers & key papers

Recommender Systems and Techniques is a topic indexed in ScholarIQ from OpenAlex.

What is known about Recommender Systems and Techniques?

ScholarIQrecord summary

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.

How many works does Recommender Systems and Techniques have?

ScholarIQindexed works

Recommender Systems and Techniques has 72 works in the ScholarIQ index. The count is the OpenAlex total, not the number of papers listed on this page.

What is the OpenAlex record for Recommender Systems and Techniques?

ScholarIQopenalex

The OpenAlex for Recommender Systems and Techniques is on the source record.

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
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 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
Time weight collaborative filtering
Yi Ding, Xue Li
2005449 Citations

Where is Recommender Systems and Techniques research published, and who funds it?

ScholarIQvenues & funding sources

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%

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