Stefano Mizzaro
Information Systems · 2017–2022
A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 19 citations into it; #4,792 of 8,771 by trust here, #6,195 by citations
- Artificial Intelligence — 16 citations into it; #24,532 of 35,345 by trust here, #30,347 by citations
- Evaluation & Benchmarks — 14 citations into it; #1,795 of 3,147 by trust here, #2,903 by citations
- Information Retrieval & Search — 12 citations into it; #661 of 1,022 by trust here, #821 by citations
- Signal Processing — 6 citations into it
Whom Stefano Mizzaro trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Stefano Mizzaro overall
Papers from Stefano Mizzaro's perspective
Ordered by how strongly the researchers Stefano Mizzaro trusts endorse each paper. Same corpus, a different order for every researcher.
- Cumulated gain-based evaluation of IR techniques
- Learning to rank using gradient descent
- Expected reciprocal rank for graded relevance
- On Crowdsourcing Relevance Magnitudes for Information Retrieval Evaluation
- Liberal relevance criteria of TREC -: counting on negligible documents?
- Handbook of Parametric and Nonparametric Statistical Procedures (Technometrics)
- Pairwise ranking aggregation in a crowdsourced setting (Web Search and Data Mining)
- 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.)
- A simple and efficient sampling method for estimating AP and NDCG
- A new rank correlation coefficient for information retrieval (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval)
Their papers
- Preferences on a Budget: Prioritizing Document Pairs when Crowdsourcing Relevance Judgments (2022) — 8 citations
- 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