Mark D. Smucker

3.0k citations

70 papers · 1.6k indexed · 1 hit paper · h-index 18

Impact in

Information Systems top 0.5%
- Information Retrieval and Search Behavior
- Web Data Mining and Analysis
- Expert finding and Q&A systems
- Recommender Systems and Techniques
Artificial Intelligence top 1%
- Topic Modeling
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques

Papers in

Information Systems 57
- Information Retrieval and Search Behavior 52
- Expert finding and Q&A systems 15
- Recommender Systems and Techniques 9
- Web Data Mining and Analysis 8
Computer Science Applications 10
- Mobile Crowdsensing and Crowdsourcing 9

Co-authors: Charles L. A. Clarke James Allan Ben Carterette Gordon V. Cormack Fernando Díaz J. Shane Culpepper Leigh Tesfatsion E. Ann Stanley
Journals: ACM SIGIR Forum (5 papers)Information Retrieval (2 papers)Biosystems (1 paper)ACM Transactions on Information Systems (1 paper)Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (1 paper)
Partner nations: Canada United States United Kingdom

In The Last Decade

Mark D. Smucker

68 papers receiving 1.5k citations

Hit Papers

align trajectories log scale

A comparison of statistical significance tests for information retrieval evaluation 2007 · 468 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2007 A comparison of statistical significance tests for information retrieval evaluation

Peers

Countries citing papers authored by Mark D. Smucker

Since Specialization

Citations

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

Fields of papers citing papers by Mark D. Smucker

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark D. Smucker. 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 Mark D. Smucker. The network helps show where Mark D. Smucker may publish in the future.

Co-authors

The 25 scholars most cited alongside Mark D. Smucker, 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 Mark D. Smucker Line = papers co-authored together Mark D. Smucker links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work
1	Overview of the TREC 2020 Health Misinformation Track. Text REtrieval Conference ·Charles L. A. Clarke, S. Ramesh, Mark D. Smucker, Maria Maistro, Guido Zuccon	2020	5
2	Evaluating sentence-level relevance feedback for high-recall information retrieval Information Retrieval ·Haotian Zhang, Gordon V. Cormack, Maura R. Grossman, Mark D. Smucker	2019	17
3	UWaterlooMDS at the TREC 2018 Common Core Track. Text REtrieval Conference ·Stefanny Espitia Concha, Gordon V. Cormack, Nimesh Ghelani, Amira Ghenai, Maura R. Grossman, Bushra Tehseen, Haotian Zhang, Mark D. Smucker	2018	2
4	Research Frontiers in Information Retrieval ACM SIGIR Forum ·J. Shane Culpepper, Fernando Díaz, Mark D. Smucker	2018	88
5	UWaterlooMDS at the TREC 2017 Common Core Track. Text REtrieval Conference ·Haotian Zhang, Stefanny Espitia Concha, Nimesh Ghelani, Min-Ho Kwon, Mark D. Smucker, Gordon V. Cormack, Maura R. Grossman	2017	5
6	Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering Haotian Zhang, Jinfeng Rao, Jimmy Lin, Mark D. Smucker	2017	3
7	Optimizing Nugget Annotations with Active Learning 相馬正己, Haotian Zhang, O. Shcherban, Jimmy Lin, Mark D. Smucker, Olga Vechtomova	2016	5
8	WaterlooClarke: TREC 2015 Total Recall Track Text REtrieval Conference ·Haotian Zhang, Lin Wu, Yipeng Wang, Charles L. A. Clarke, Mark D. Smucker	2015	1
9	Overview of the TREC 2012 Crowdsourcing Track. Text REtrieval Conference ·Mark D. Smucker, Gabriella Kazai, Matthew Lease	2012	4
10	Modeling user variance in time-biased gain Mark D. Smucker, Charles L. A. Clarke	2012	20
11	Human question answering performance using an interactive document retrieval system Mark D. Smucker, James Allan, V. P. Krasnianskiy	2012	5
12	Crowdsourcing with a Crowd of One and Other TREC 2011 Crowdsourcing and Web Track Experiments. Text REtrieval Conference ·Mark D. Smucker	2011	2
13	Report on the SIGIR 2010 workshop on the simulation of interaction ACM SIGIR Forum ·Leif Azzopardi, Kalervo Järvelin, Jaap Kamps, Mark D. Smucker	2011	34
14	University of Waterloo at TREC 2010: Legal Interactive Text REtrieval Conference ·Mark D. Smucker, Charles L. A. Clarke, Gordon V. Cormack, Olga Vechtomova	2010	2
15	Experiments with ClueWeb09: Relevance Feedback and Web Tracks Text REtrieval Conference ·Mark D. Smucker, Charles L. A. Clarke, Gordon V. Cormack	2009	6
16	UMass Complex Interactive Question Answering (ciQA) 2007: Human Performance as Question Answerers Text REtrieval Conference ·Mark D. Smucker, James Allan, V. P. Krasnianskiy	2007	1
17	Using similarity links as shortcuts to relevant web pages Mark D. Smucker, James Allan	2007	6
18	Find-similar Mark D. Smucker, James Allan	2006	41
19	UMass Genomics 2006: Query-Biased Pseudo Relevance Feedback Text REtrieval Conference ·Mark D. Smucker	2006	3
20	Preferential partner selection in an evolutionary study of Prisoner's Dilemma Biosystems ·Dan Ashlock, Mark D. Smucker, E. Ann Stanley, Leigh Tesfatsion	1996	93

About Mark D. Smucker

Mark D. Smucker is a scholar working on Information Systems, Computer Science Applications, Artificial Intelligence, General Decision Sciences and Management Science and Operations Research, having authored 70 papers that have together received 1.6k indexed citations. Recurring topics across this work include Information Retrieval and Search Behavior (52 papers), Topic Modeling (25 papers), Expert finding and Q&A systems (15 papers), Mobile Crowdsensing and Crowdsourcing (9 papers), Recommender Systems and Techniques (9 papers), Natural Language Processing Techniques (9 papers), Web Data Mining and Analysis (8 papers) and Data Quality and Management (8 papers). The work is most often cited by research in Information Systems (958 citations), Artificial Intelligence (915 citations), Computer Science Applications (154 citations), Management Science and Operations Research (187 citations) and Signal Processing (129 citations). Mark D. Smucker has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Charles L. A. Clarke, James Allan, Ben Carterette, Gordon V. Cormack, Fernando Díaz, J. Shane Culpepper, Leigh Tesfatsion, E. Ann Stanley, Dan Ashlock and Haotian Zhang. Their work appears in journals such as ACM SIGIR Forum, Information Retrieval, Biosystems, ACM Transactions on Information Systems and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

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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