Yuanhua Lv

1.7k citations
38 papers · 1.0k · h-index 17

Impact in

    • Information Retrieval and Search Behavior
    • Web Data Mining and Analysis
    • Recommender Systems and Techniques
    • Topic Modeling
    • Natural Language Processing Techniques
    • Text and Document Classification Technologies

Papers in

    • Information Retrieval and Search Behavior 20
    • Web Data Mining and Analysis 9
    • Recommender Systems and Techniques 5
    • Expert finding and Q&A systems 4
    • Topic Modeling 21
    • Natural Language Processing Techniques 6

Yuanhua Lv

36 papers receiving 978 citations

Peers

Yuanhua Lv
Comparison fields: 5 of 58
  • Information Systems 628
  • Artificial Intelligence 757
  • Computer Vision and Pattern Recognition 223
  • Signal Processing 108
  • Management Science and Operations Research 76
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Citations per field
00.5×1.5×1.9×
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Citations per year

Countries citing papers authored by Yuanhua Lv

Since Specialization
Citations

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

Fields of papers citing papers by Yuanhua Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yuanhua Lv, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yuanhua Lv Line = papers co-authored together Yuanhua Lv links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009146
2 2010127
3 2009118
4 201181
5 201166
6 200963
7 201161
8 201841
9 201130
10 201725
11 201925
12 201825
13 201124
14 201524
15 201422
16 201520
17 201517
18 201216
19 200616
20 20169

About Yuanhua Lv

Yuanhua Lv is a scholar working on Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Computer Networks and Communications, having authored 38 papers that have together received 1.0k indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Information Retrieval and Search Behavior (20 papers), Web Data Mining and Analysis (9 papers), Natural Language Processing Techniques (6 papers), Recommender Systems and Techniques (5 papers), Image Retrieval and Classification Techniques (5 papers), Complex Network Analysis Techniques (5 papers) and Expert finding and Q&A systems (4 papers). The work is most often cited by research in Information Systems (628 citations), Artificial Intelligence (757 citations), Computer Vision and Pattern Recognition (223 citations), Signal Processing (108 citations) and Management Science and Operations Research (76 citations). Yuanhua Lv has collaborated with scholars based in United States, China and Italy. Frequent co-authors include ChengXiang Zhai, Philip S. Yu, Jiawei Zhang, Wan Chen, Yi Chang, Nan Duan, Duyu Tang, Zhaohui Zheng, Pranam Kolari and Taesup Moon. Their work appears in journals such as Information Retrieval, ACM Transactions on Information Systems, IEEE Transactions on Knowledge and Data Engineering, ACM SIGIR Forum and Agriculture.

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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