K. Roitero, M. Soprano, A. Brunello, and S. Mizzaro. Reproduce and improve: An evolutionary approach to select a few good topics for information retrieval evaluation. ACM Journal of Data and Information Quality (JDIQ), 10(3):12, 2018.
Categoria: Publication
Reproduce. generalize. extend. on information retrieval evaluation without relevance judgments
K. Roitero, M. Passon, G. Serra, and S. Mizzaro. Reproduce. generalize. extend. on information retrieval evaluation without relevance judgments. ACM Journal of Data and Information Quality (JDIQ), 10(3):11, 2018.
Evaluation in academic publishing: Crowdsourcing peer review?
S. Mizzaro. Evaluation in academic publishing: Crowdsourcing peer review? ERCIM NEWS, (113):11– 12, 2018.
On crowdsourcing relevance magnitudes for information retrieval evaluation
E. Maddalena, S. Mizzaro, F. Scholer, and A. Turpin. On crowdsourcing relevance magnitudes for information retrieval evaluation. ACM Transactions on Information Systems (TOIS), 35(3):19, 2017.
Mobile Information Retrieval
F. Crestani, S. Mizzaro, I. Scagnetto. Mobile Information Retrieval. In SpringerBriefs in Computer Science, ISSN 2191-5768, Softcover ISBN 978-3-319-60776-4, eBook ISBN 978-3-319-60777-1, DOI 10.1007/978-3-319-60777-1, Springer International Publishing 2017.
Mining Movement Data to Extract Personal Points of Interest: A Feature Based Approach
Marco Pavan, Stefano Mizzaro, Ivan Scagnetto. Mining Movement Data to Extract Personal Points of Interest: A Feature Based Approach. In Studies in Computational Intelligence, Volume 668, pp. 35-61, Springer 2017, ISBN: 978-3-319-46133-5 (Print) 978-3-319-46135-9 (Online), doi: 10.1007/978-3-319-46135-9.
Exploiting News to Categorize Tweets: Quantifying the Impact of Different News Collections
M. Pavan, S. Mizzaro, M. Bernardon, I. Scagnetto. Exploiting News to Categorize Tweets: Quantifying the Impact of Different News Collections. In Proceedings of NewsIR’16, Padua, Italy, March 20, 2016, CEUR WS Vol-1568, ISSN 1613-0073.
Finding Important Locations: A Feature-Based Approach
M. Pavan, S. Mizzaro, I. Scagnetto and A. Beggiato. Finding Important Locations: A Feature-Based Approach. In Proceedings of Mobile Data Management (MDM) 2015, 15–18 June, 2015, Pittsburgh, Pennsylvania, USA, Vol. 1, pp. 110–115, http://dx.doi.org/10.1109/MDM.2015.11, ISBN: 978-1-4799-9971-2, IEEE.
Content-Based Similarity of Twitter Users
S. Mizzaro, M. Pavan and I. Scagnetto. Content-Based Similarity of Twitter Users. In Proc. of ECIR 2015, 37th European Conference on IR Research, Vienna, Austria, March 29 – April 2, 2015. Lecture Notes in Computer Science Volume 9022, 2015, pages 507-512.
A context-aware retrieval system for mobile applications
S. Mizzaro, M. Pavan, I. Scagnetto, and I. Zanello. A context-aware retrieval system for mobile applications. In Proceedings of the 4th Workshop on Context-Awareness in Retrieval and Recommendation (CARR ’14). ACM, New York, NY, USA, 18-25. DOI=10.1145/2601301.2601305 http://doi.acm.org/10.1145/2601301.2601305