Lan Nie
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
- Web Data Mining and Analysis
- Artificial Intelligence top 5%
- Topic Modeling
- Text and Document Classification Technologies
Papers in
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- Spam and Phishing Detection 4
- Web Data Mining and Analysis 4
- Information Retrieval and Search Behavior 2
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- Data Stream Mining Techniques 2
- Text and Document Classification Technologies 2
- Topic Modeling 2
- Co-authors
- Brian D. Davison (7 shared papers)Xiaoguang Qi (2 shared papers)Martin Wattenberg (1 shared paper)Dan Liu (1 shared paper)D. Sculley (1 shared paper)Michael Young (1 shared paper)Dietmar Ebner (1 shared paper)Jeremy Kubica (1 shared paper)
- Journals
- International Conference on Computational Linguistics (1 paper)National Conference on Artificial Intelligence (1 paper)Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)
- Partner nations
- United States
In The Last Decade
Lan Nie
12 papers receiving 632 citations
Lan Nie's Hit Papers
Peers
Comparison fields: 5 of 76
- Information Systems 379
- Artificial Intelligence 361
- Management Science and Operations Research 132
- Marketing 95
- Computational Mathematics 5
Countries citing papers authored by Lan Nie
This map shows the geographic impact of Lan Nie'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 Lan Nie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lan Nie more than expected).
Fields of papers citing papers by Lan Nie
This network shows the impact of papers produced by Lan Nie. 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 Lan Nie. The network helps show where Lan Nie may publish in the future.
Co-authors
The 23 scholars most cited alongside Lan Nie, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Ad click prediction Hit paper breakdown → | 2013 | 509 |
| 2 | 2006 | 85 | |
| 3 | Resolving Surface Forms to Wikipedia Topics | 2010 | 43 |
| 4 | 2007 | 13 | |
| 5 | 2007 | 13 | |
| 6 | From whence does your authority come?: utilizing community relevance in ranking | 2007 | 11 |
| 7 | 2007 | 8 | |
| 8 | 2022 | 8 | |
| 9 | 2008 | 6 | |
| 10 | 2007 | 3 | |
| 11 | 2005 | 2 | |
| 12 | 2010 | 1 |
About Lan Nie
Lan Nie is a scholar working on Information Systems, Artificial Intelligence, Statistical and Nonlinear Physics, Management Science and Operations Research and Communication, having authored 12 papers that have together received 702 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (4 papers), Spam and Phishing Detection (4 papers), Web Data Mining and Analysis (4 papers), Data Stream Mining Techniques (2 papers), Advanced Bandit Algorithms Research (2 papers), Text and Document Classification Technologies (2 papers), Topic Modeling (2 papers) and Information Retrieval and Search Behavior (2 papers). The work is most often cited by research in Information Systems (379 citations), Artificial Intelligence (361 citations), Management Science and Operations Research (132 citations), Marketing (95 citations) and Computational Mathematics (5 citations). Lan Nie has collaborated with scholars based in United States. Frequent co-authors include Brian D. Davison, Xiaoguang Qi, Martin Wattenberg, Dan Liu, D. Sculley, Michael Young, Dietmar Ebner, Jeremy Kubica, Gary D. Holt and Todd Phillips. Their work appears in journals such as International Conference on Computational Linguistics, National Conference on Artificial Intelligence and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
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.