Jonathan L. Elsas

741 total citations
16 papers, 396 citations indexed

About

Jonathan L. Elsas is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jonathan L. Elsas has authored 16 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Information Systems, 12 papers in Artificial Intelligence and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jonathan L. Elsas's work include Information Retrieval and Search Behavior (9 papers), Topic Modeling (6 papers) and Web Data Mining and Analysis (5 papers). Jonathan L. Elsas is often cited by papers focused on Information Retrieval and Search Behavior (9 papers), Topic Modeling (6 papers) and Web Data Mining and Analysis (5 papers). Jonathan L. Elsas collaborates with scholars based in United States. Jonathan L. Elsas's co-authors include Jaime Carbonell, Susan Dumais, Jaime Arguello, Jamie Callan, Jaime Teevan, Eytan Adar, Vitor R. Carvalho, Eric Nyberg, William W. Cohen and Gary Marchionini and has published in prestigious journals such as Computer, Figshare and Proceedings of the International AAAI Conference on Web and Social Media.

In The Last Decade

Jonathan L. Elsas

14 papers receiving 368 citations

Peers

Jonathan L. Elsas

IS

AI

CNC

CVPR

SP

Pu‐Jen Cheng Taiwan

Stanisław Osiński Poland

Kenneth Wai-Ting Leung Hong Kong

Roberto Mirizzi Italy

Liyun Ru China

Ciya Liao United States

Reiner Kraft United States

Cheng Xiang Zhai United States

Paul - Alexandru Chirita Germany

Xuezhi Cao China

Pu‐Jen Cheng Taiwan

Jonathan L. Elsas

203 ×0.7

IS

267 ×1.2

AI

39 ×0.7

CNC

82 ×1.4

CVPR

44 ×0.8

SP

Citations per year, relative to Jonathan L. Elsas Jonathan L. Elsas (= 1×) peers Pu‐Jen Cheng

Countries citing papers authored by Jonathan L. Elsas

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan L. Elsas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan L. Elsas. 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 Jonathan L. Elsas. The network helps show where Jonathan L. Elsas may publish in the future.

Co-authorship network of co-authors of Jonathan L. Elsas

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

All Works

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16 of 16 papers shown

1.

Arguello, Jaime, Jonathan L. Elsas, Jamie Callan, & Jaime Carbonell. (2021). Document Representation and Query Expansion Models for Blog Recommendation. Proceedings of the International AAAI Conference on Web and Social Media. 2(1). 10–18. 3 indexed citations

2.

Elsas, Jonathan L.. (2019). An Evaluation of Projection Techniques for Document Clustering: Latent Semantic Analysis and Independent Component Analysis. Carolina Digital Repository (University of North Carolina at Chapel Hill).

3.

Elsas, Jonathan L., Pınar Dönmez, Jamie Callan, & Jaime Carbonell. (2018). Pairwise Document Classification for Relevance Feedback. Figshare.

4.

Elsas, Jonathan L., Jaime Arguello, Jamie Callan, & Jaime Carbonell. (2018). Retrieval and Feedback Models for Blog Distillation. Figshare. 1 indexed citations

5.

Elsas, Jonathan L. & Susan Dumais. (2010). Leveraging temporal dynamics of document content in relevance ranking. 1–10. 58 indexed citations

6.

Elsas, Jonathan L., et al.. (2010). Rank learning for factoid question answering with linguistic and semantic constraints. 459–468. 34 indexed citations

7.

Elsas, Jonathan L. & Natalie Glance. (2010). Shopping for top forums. 23–30. 4 indexed citations

8.

Adar, Eytan, Jaime Teevan, Susan Dumais, & Jonathan L. Elsas. (2009). The web changes everything. 282–291. 92 indexed citations

9.

Elsas, Jonathan L. & Jaime Carbonell. (2009). It pays to be picky. 714–715. 32 indexed citations

10.

Elsas, Jonathan L., Vitor R. Carvalho, & Jaime Carbonell. (2008). Fast learning of document ranking functions with the committee perceptron. 55–55. 29 indexed citations

11.

Carvalho, Vitor R., Jonathan L. Elsas, William W. Cohen, & Jaime Carbonell. (2008). A Meta-Learning Approach for Robust Rank Learning. 20 indexed citations

12.

Arguello, Jaime, et al.. (2008). Document and Query Expansion Models for Blog Distillation. 3 indexed citations

13.

Elsas, Jonathan L., Jaime Arguello, Jamie Callan, & Jaime Carbonell. (2008). Retrieval and feedback models for blog feed search. 347–354. 97 indexed citations

14.

Carvalho, Vitor R., Jonathan L. Elsas, William W. Cohen, & Jaime Carbonell. (2008). Suppressing outliers in pairwise preference ranking. 1487–1488. 3 indexed citations

15.

Marchionini, Gary, et al.. (2005). Accessing government statistical information. Computer. 38(12). 52–61. 8 indexed citations

16.

Efron, Miles, Jonathan L. Elsas, Gary Marchionini, & J. L. Zhang. (2004). Machine learning for information architecture in a large governmental website. 151–159. 12 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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