Tun Lu

2.2k total citations · 1 hit paper
124 papers, 1.3k citations indexed

About

Tun Lu is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Tun Lu has authored 124 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Information Systems, 43 papers in Artificial Intelligence and 27 papers in Computer Networks and Communications. Recurrent topics in Tun Lu's work include Recommender Systems and Techniques (35 papers), Topic Modeling (12 papers) and Service-Oriented Architecture and Web Services (11 papers). Tun Lu is often cited by papers focused on Recommender Systems and Techniques (35 papers), Topic Modeling (12 papers) and Service-Oriented Architecture and Web Services (11 papers). Tun Lu collaborates with scholars based in China, United States and United Kingdom. Tun Lu's co-authors include Ning Gu, Dongsheng Li, Peng Zhang, Li Shang, Qin Lv, Hansu Gu, Xianghua Ding, Dongsheng Li, Jie Sui and Yixuan Chen and has published in prestigious journals such as PLoS ONE, International Journal of Molecular Sciences and Computers in Human Behavior.

In The Last Decade

Tun Lu

112 papers receiving 1.3k citations

Hit Papers

Cross-modal Ambiguity Learning for Multimodal Fake News D... 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Cross-modal Ambiguity Learning for Multimodal Fake News Detection
    2022 166 citations Peng Zhang, Qin Lv et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tun Lu China 20 609 496 313 149 148 124 1.3k
Bebo White United States 10 508 0.8× 778 1.6× 142 0.5× 108 0.7× 171 1.2× 39 1.6k
Yen-Ting Lin Taiwan 20 682 1.1× 204 0.4× 190 0.6× 142 1.0× 109 0.7× 68 1.6k
Joseph F. McCarthy United States 18 312 0.5× 290 0.6× 220 0.7× 138 0.9× 83 0.6× 42 1.1k
Animesh Mukherjee India 20 244 0.4× 863 1.7× 200 0.6× 94 0.6× 167 1.1× 158 1.7k
Costas Vassilakis Greece 21 654 1.1× 648 1.3× 275 0.9× 488 3.3× 382 2.6× 162 1.8k
Chenhao Tan United States 22 242 0.4× 847 1.7× 239 0.8× 108 0.7× 39 0.3× 90 1.5k
Tien Thanh Nguyen Australia 21 419 0.7× 567 1.1× 180 0.6× 211 1.4× 101 0.7× 57 1.2k
Jong Woo Kim South Korea 19 240 0.4× 344 0.7× 290 0.9× 239 1.6× 59 0.4× 117 1.3k
Ziwei Ji Hong Kong 10 259 0.4× 1.3k 2.6× 142 0.5× 170 1.1× 53 0.4× 22 2.1k
Tiezheng Yu Hong Kong 11 277 0.5× 1.4k 2.8× 157 0.5× 204 1.4× 52 0.4× 19 2.2k

Countries citing papers authored by Tun Lu

Since Specialization
Citations

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

Fields of papers citing papers by Tun Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tun Lu

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

All Works

20 of 20 papers shown
1.
Li, Dongsheng, Hansu Gu, Peng Zhang, et al.. (2025). AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations. 2566–2571.
2.
Zhang, Peng, et al.. (2025). WePilot: Integrating Younger Family Members and Chatbot to Support Older Adults Learning Smartphone Usage. Proceedings of the ACM on Human-Computer Interaction. 9(7). 1–34.
3.
Li, Dongsheng, Hansu Gu, Tun Lu, et al.. (2025). Frequency-aware Graph Signal Processing for Collaborative Filtering. 1417–1421.
4.
Zhang, Peng, Dongsheng Li, Hansu Gu, et al.. (2025). Filtering Discomforting Recommendations with Large Language Models. 3639–3650.
5.
Li, Dongsheng, Hansu Gu, Tun Lu, et al.. (2025). Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation. 363–372.
6.
Li, Dongsheng, et al.. (2025). FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation. 1687–1696. 1 indexed citations
7.
Li, Yaqiong, Tun Lu, Weimin Li, & Peng Zhang. (2024). HCCKshell: A heterogeneous cross-comparison improved Kshell algorithm for Influence Maximization. Information Processing & Management. 61(3). 103681–103681. 8 indexed citations
8.
Lu, Tun, et al.. (2024). Advancing cancer nanomedicine with machine learning. Acta Pharmaceutica Sinica B. 14(9). 4183–4185. 1 indexed citations
9.
Li, Dongsheng, Hansu Gu, Tun Lu, et al.. (2024). Hierarchical Graph Signal Processing for Collaborative Filtering. 3229–3240. 4 indexed citations
10.
Li, Dongsheng, et al.. (2024). Simulating News Recommendation Ecosystems for Insights and Implications. IEEE Transactions on Computational Social Systems. 11(5). 5699–5713. 8 indexed citations
11.
Zhang, Peng, et al.. (2024). DeLink: An Adversarial Framework for Defending against Cross-site User Identity Linkage. ACM Transactions on the Web. 18(2). 1–34. 1 indexed citations
12.
Gu, Hansu, et al.. (2024). RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM Agents. IEEE Transactions on Computational Social Systems. 11(5). 6759–6770. 15 indexed citations
13.
Li, Dongsheng, et al.. (2024). Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation. ACM Transactions on the Web. 18(3). 1–33. 6 indexed citations
14.
Liu, S. B., Hansu Gu, Dongsheng Li, et al.. (2023). AutoSeqRec: Autoencoder for Efficient Sequential Recommendation. 1493–1502. 17 indexed citations
15.
Li, Dongsheng, Hansu Gu, Tun Lu, et al.. (2023). Personalized Graph Signal Processing for Collaborative Filtering. arXiv (Cornell University). 1264–1272. 17 indexed citations
16.
Li, Dongsheng, Hansu Gu, Tun Lu, et al.. (2023). Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation. 1086–1095. 5 indexed citations
17.
Zhao, Yingying, Rui Zhu, Qin Lv, et al.. (2023). CASES. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 7(3). 1–31. 6 indexed citations
18.
Zhang, Peng, et al.. (2022). Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning. ACM Transactions on Information Systems. 40(4). 1–28. 2 indexed citations
19.
Zhang, Peng, et al.. (2022). Exploring How Workspace Awareness Cues Affect Distributed Meeting Outcome. International Journal of Human-Computer Interaction. 39(8). 1606–1625. 3 indexed citations
20.
Zhao, Yingying, Ning Li, Yujiang Wang, et al.. (2022). Unveiling Causal Attention in Dogs' Eyes with Smart Eyewear. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 6(4). 1–33.

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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