Anjing Luo

420 total citations · 1 hit paper
2 papers, 269 citations indexed

About

Anjing Luo is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Anjing Luo has authored 2 papers receiving a total of 269 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Information Systems, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Signal Processing. Recurrent topics in Anjing Luo's work include Recommender Systems and Techniques (2 papers), Image Retrieval and Classification Techniques (1 paper) and Data Management and Algorithms (1 paper). Anjing Luo is often cited by papers focused on Recommender Systems and Techniques (2 papers), Image Retrieval and Classification Techniques (1 paper) and Data Management and Algorithms (1 paper). Anjing Luo collaborates with scholars based in China, United States and Australia. Anjing Luo's co-authors include Victor S. Sheng, Yanchi Liu, Jiajie Xu, Pengpeng Zhao, Fuzhen Zhuang, Xiaofang Zhou, Zhixu Li, Deqing Wang and Junhua Fang and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Anjing Luo

2 papers receiving 264 citations

Hit Papers

Where to Go Next: A Spatio-Temporal Gated Network for Nex... 2020 2026 2022 2024 2020 50 100 150 200

Peers

Anjing Luo
Jarana Manotumruksa United Kingdom
Yuntao Du China
Renjun Hu China
Tim Hanratty United States
Jarana Manotumruksa United Kingdom
Anjing Luo
Citations per year, relative to Anjing Luo Anjing Luo (= 1×) peers Jarana Manotumruksa

Countries citing papers authored by Anjing Luo

Since Specialization
Citations

This map shows the geographic impact of Anjing Luo'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 Anjing Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anjing Luo more than expected).

Fields of papers citing papers by Anjing Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anjing Luo. 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 Anjing Luo. The network helps show where Anjing Luo may publish in the future.

Co-authorship network of co-authors of Anjing Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Anjing Luo. A scholar is included among the top collaborators of Anjing Luo 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 Anjing Luo. Anjing Luo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

2 of 2 papers shown
1.
Zhao, Pengpeng, Anjing Luo, Yanchi Liu, et al.. (2020). Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation. IEEE Transactions on Knowledge and Data Engineering. 34(5). 2512–2524. 210 indexed citations breakdown →
2.
Luo, Anjing, Pengpeng Zhao, Yanchi Liu, et al.. (2020). Collaborative Self-Attention Network for Session-based Recommendation. 2591–2597. 59 indexed citations

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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