E. Amigó, H. Fang, S. Mizzaro, and C. Zhai. Axiomatic thinking for information retrieval: And related tasks. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1419–1420. ACM, 2017.
Autore: ivan_scagnetto
Report on the sigir 2017 workshop on axiomatic thinking for information retrieval and related tasks (ATIR)
E. Amigó, H. Fang, S. Mizzaro, and C. Zhai. Report on the sigir 2017 workshop on axiomatic thinking for information retrieval and related tasks (ATIR). In ACM SIGIR Forum, volume 51, pages 99–106. ACM, 2018.
Reproduce and improve: An evolutionary approach to select a few good topics for information retrieval evaluation
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.
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.