Eddy Maddalena

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 3 papers; trusts 2 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 11 citations into it; #22,656 of 35,345 by trust here, #30,347 by citations
• Evaluation & Benchmarks — 10 citations into it; #1,640 of 3,147 by trust here, #2,903 by citations
• Information Systems — 10 citations into it; #4,268 of 8,771 by trust here, #6,501 by citations
• Information Retrieval & Search — 8 citations into it; #615 of 1,022 by trust here, #844 by citations

Whom Eddy Maddalena trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Eero Sormunen
2. Tefko Saracevic
3. Klaus Krippendorff
4. Kalervo Järvelin

Who trusts Eddy Maddalena overall

1. Kevin Roitero
2. C. Clarke
3. Cheng-hsin Luo
4. Alessandro Checco
5. Stefano Mizzaro
6. Gianluca Demartini
7. Xinyi Yan
8. Ziying Yang
9. Mark D. Smucker

Papers from Eddy Maddalena's perspective

Ordered by how strongly the researchers Eddy Maddalena trusts endorse each paper. Same corpus, a different order for every researcher.

1. Cumulated gain-based evaluation of IR techniques
2. Learning to rank using gradient descent
3. Expected reciprocal rank for graded relevance
4. Liberal relevance criteria of TREC -: counting on negligible documents?
5. Handbook of Parametric and Nonparametric Statistical Procedures (Technometrics)
6. On Crowdsourcing Relevance Magnitudes for Information Retrieval Evaluation
7. Relevance: A review of the literature and a framework for thinking on the notion in information science. Part II: nature and manifestations of relevance (J. Assoc. Inf. Sci. Technol.)
8. Variations in relevance judgments and the measurement of retrieval effectiveness
9. Overview of the seventh text retrieval conference (trec-7) [on-line]
10. How reliable are the results of large-scale information retrieval experiments?

Their papers

• On the effect of relevance scales in crowdsourcing relevance assessments for Information Retrieval evaluation (2021) — 24 citations
• On Fine-Grained Relevance Scales (2018) — 28 citations
• On Crowdsourcing Relevance Magnitudes for Information Retrieval Evaluation (2017) — 96 citations