VLDB 2026 Research / reviewers in the wild / expert
Hongru Lu
dblp:325/4501
· DBLP profile ↗
6ranked-venue papers
3as first author
6since 2021 · last 2026
0000-0002-7288-5296ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3 · 3 since 2021Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Data-centric cross-city knowledge transfer for regional socioeconomic prediction
Hongru Lu, Dong Kan, Xiao Jing, Zhiang Wu 0001 |
Knowl. Based Syst. | 1 |
| 2026 | Few-shot urban strategic group discovery with LLM annotation and hypergraph learning
Hongru Lu, Xiao Jing, Zhiang Wu 0001 |
World Wide Web (WWW) | 1 |
| 2025 | Revisiting intelligent audit from a data science perspective
Hongru Lu, Zhiang Wu 0001 |
Neurocomputing | 1 |
| 2024 | Learning context-aware region similarity with effective spatial normalization over Point-of-Interest data
Jiahui Jin 0001, Yifan Song 0003, Dong Kan, Binjie Zhang, Jinghui Zhang 0001, Hongru Lu |
Inf. Process. Manag. | 7 |
| 2023 | Modeling the information behavior patterns of new graduate students in supervisor selection
Juan Xie, Hongru Lu, Ying Cheng 0002 |
Inf. Process. Manag. | 4 |
| 2022 | Citing criteria and its effects on researcher's intention to cite: A mixed-method studyabstractAbstract This study explored users' criteria for citation decisions and investigated the effects on users' intention to cite using a mixed‐method approach. A qualitative study was conducted first, where 16 citing criteria were identified based on interviews and inductive analysis. The findings were then used to develop hypotheses and extend the information adoption model. A questionnaire was designed to collect data from users in Chinese universities to test the research model. The findings indicated that pleasure, topicality, and functionality significantly increased users' perceived information usefulness, while familiarity and accessibility significantly enhanced users' perceived ease of use. Information usefulness and information ease of use further contributed to users' intention to cite with adjusted R2 equaling 44.6%. It is also found that perceived academic quality based on 5 antecedents (i.e., reliability, comprehensiveness, novelty, author credibility, and source reputation) significantly increased users' pleasure. Implications and limitations were provided. Juan Xie, Hongru Lu, Lele Kang, Ying Cheng 0002 |
J. Assoc. Inf. Sci. Technol. | 2 |