Shiwen Wu

1.7k citations
6 papers · 951 indexed · 2 hit papers · h-index 5
Topics
Recommender Systems and Techniques (5 papers)Advanced Graph Neural Networks (2 papers)Advanced Bandit Algorithms Research (2 papers)
Partner nations
ChinaUnited StatesSpain

In The Last Decade

Shiwen Wu

6 papers receiving 924 citations

Hit Papers

Graph Neural Networks in Recommender Systems: A Survey202220262023202420222022100200300400500

Peers

Shiwen Wu
Comparison fields: 5 of 75
  • Artificial Intelligence 680
  • Information Systems 628
  • Computer Vision and Pattern Recognition 208
  • Management Science and Operations Research 105
  • Computer Networks and Communications 86
Replace Pipei Huang with:
Pipei Huang China
Xin Xin China
Yukuo Cen China
Eric Zhao United States
Tao Qi China
Jianxin Ma China
Mohsen Afsharchi Iran
Yue Hu China
Shiwen Wu relative to Pipei Huang China Pipei Huang's profile →
Citations per field
00.5×2.6×
Pipei Huang · 1×
Citations per year

Countries citing papers authored by Shiwen Wu

Since Specialization
Citations

This map shows the geographic impact of Shiwen Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shiwen Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shiwen Wu more than expected).

Fields of papers citing papers by Shiwen Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shiwen Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shiwen Wu. The network helps show where Shiwen Wu may publish in the future.

Co-authorship network of co-authors of Shiwen Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Shiwen Wu. A scholar is included among the top collaborators of Shiwen Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shiwen Wu. Shiwen Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

6 of 6 papers shown
#WorkIndexed citations
1 2
2
Graph Neural Networks in Recommender Systems: A Surveybreakdown →
594
3
Contrastive Learning for Sequential Recommendationbreakdown →
301
4 5
5 4
6 45

About Shiwen Wu

Shiwen Wu is a scholar working on Computational Mathematics, Management Science and Operations Research and Information Systems, having authored 6 papers that have together received 951 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (5 papers), Advanced Graph Neural Networks (2 papers) and Advanced Bandit Algorithms Research (2 papers). The work is most often cited by research in Information Systems (628 citations), Artificial Intelligence (680 citations) and Computer Vision and Pattern Recognition (208 citations). Shiwen Wu has collaborated with scholars based in China, United States and Spain. Frequent co-authors include Bin Cui, Fei Sun, Wentao Zhang, X. H. Xie, Xu Xie, Jinyang Gao, Jiandong Zhang, Zhaoyang Liu, Bolin Ding and Kaigui Bian. Their work appears in journals such as ACM Computing Surveys, Knowledge-Based Systems and Proceedings of the VLDB Endowment.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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