Elenco (non esaustivo) di pubblicazioni prodotte dai membri del laboratorio
2018
Roitero, Kevin; Maddalena, Eddy; Ponte, Yannick; Mizzaro, Stefano
IRevalOO: An Object Oriented Framework for Retrieval Evaluation Proceedings Article
In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 913–916, Association for Computing Machinery, Ann Arbor, MI, USA, 2018, ISBN: 9781450356572.
@inproceedings{10.1145/3209978.3210084,
title = {IRevalOO: An Object Oriented Framework for Retrieval Evaluation},
author = {Kevin Roitero and Eddy Maddalena and Yannick Ponte and Stefano Mizzaro},
url = {https://doi.org/10.1145/3209978.3210084},
doi = {10.1145/3209978.3210084},
isbn = {9781450356572},
year = {2018},
date = {2018-01-01},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {913–916},
publisher = {Association for Computing Machinery},
address = {Ann Arbor, MI, USA},
series = {SIGIR '18},
abstract = {We propose IRevalOO, a flexible Object Oriented framework that (i) can be used as-is as a replacement of the widely adopted trec_eval software, and (ii) can be easily extended (or "instantiated'', in framework terminology) to implement different scenarios of test collection based retrieval evaluation. Instances of IRevalOO can provide a usable and convenient alternative to the state-of-the-art software commonly used by different initiatives (TREC, NTCIR, CLEF, FIRE, etc.). Also, those instances can be easily adapted to satisfy future customization needs of researchers, as: implementing and experimenting with new metrics, even based on new notions of relevance; using different formats for system output and "qrels''; and in general visualizing, comparing, and managing retrieval evaluation results.},
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We propose IRevalOO, a flexible Object Oriented framework that (i) can be used as-is as a replacement of the widely adopted trec_eval software, and (ii) can be easily extended (or "instantiated'', in framework terminology) to implement different scenarios of test collection based retrieval evaluation. Instances of IRevalOO can provide a usable and convenient alternative to the state-of-the-art software commonly used by different initiatives (TREC, NTCIR, CLEF, FIRE, etc.). Also, those instances can be easily adapted to satisfy future customization needs of researchers, as: implementing and experimenting with new metrics, even based on new notions of relevance; using different formats for system output and "qrels''; and in general visualizing, comparing, and managing retrieval evaluation results.
Roitero, Kevin
CHEERS: CHeap & Engineered Evaluation of Retrieval Systems Proceedings Article
In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1467, Association for Computing Machinery, Ann Arbor, MI, USA, 2018, ISBN: 9781450356572.
@inproceedings{10.1145/3209978.3210229,
title = {CHEERS: CHeap & Engineered Evaluation of Retrieval Systems},
author = {Kevin Roitero},
url = {https://doi.org/10.1145/3209978.3210229},
doi = {10.1145/3209978.3210229},
isbn = {9781450356572},
year = {2018},
date = {2018-01-01},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {1467},
publisher = {Association for Computing Machinery},
address = {Ann Arbor, MI, USA},
series = {SIGIR '18},
abstract = {In test collection based evaluation of retrieval effectiveness, many research investigated different directions for an economical and a semi-automatic evaluation of retrieval systems. Although several methods have been proposed and experimentally evaluated, their accuracy seems still limited. In this paper we present our proposal for a more engineered approach to information retrieval evaluation.},
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In test collection based evaluation of retrieval effectiveness, many research investigated different directions for an economical and a semi-automatic evaluation of retrieval systems. Although several methods have been proposed and experimentally evaluated, their accuracy seems still limited. In this paper we present our proposal for a more engineered approach to information retrieval evaluation.
Roitero, Kevin; Maddalena, Eddy; Demartini, Gianluca; Mizzaro, Stefano
On Fine-Grained Relevance Scales Proceedings Article
In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 675–684, Association for Computing Machinery, Ann Arbor, MI, USA, 2018, ISBN: 9781450356572.
@inproceedings{10.1145/3209978.3210052,
title = {On Fine-Grained Relevance Scales},
author = {Kevin Roitero and Eddy Maddalena and Gianluca Demartini and Stefano Mizzaro},
url = {https://doi.org/10.1145/3209978.3210052},
doi = {10.1145/3209978.3210052},
isbn = {9781450356572},
year = {2018},
date = {2018-01-01},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {675–684},
publisher = {Association for Computing Machinery},
address = {Ann Arbor, MI, USA},
series = {SIGIR '18},
abstract = {In Information Retrieval evaluation, the classical approach of adopting binary relevance judgments has been replaced by multi-level relevance judgments and by gain-based metrics leveraging such multi-level judgment scales. Recent work has also proposed and evaluated unbounded relevance scales by means of Magnitude Estimation (ME) and compared them with multi-level scales. While ME brings advantages like the ability for assessors to always judge the next document as having higher or lower relevance than any of the documents they have judged so far, it also comes with some drawbacks. For example, it is not a natural approach for human assessors to judge items as they are used to do on the Web (e.g., 5-star rating). In this work, we propose and experimentally evaluate a bounded and fine-grained relevance scale having many of the advantages and dealing with some of the issues of ME. We collect relevance judgments over a 100-level relevance scale (S100) by means of a large-scale crowdsourcing experiment and compare the results with other relevance scales (binary, 4-level, and ME) showing the benefit of fine-grained scales over both coarse-grained and unbounded scales as well as highlighting some new results on ME. Our results show that S100 maintains the flexibility of unbounded scales like ME in providing assessors with ample choice when judging document relevance (i.e., assessors can fit relevance judgments in between of previously given judgments). It also allows assessors to judge on a more familiar scale (e.g., on 10 levels) and to perform efficiently since the very first judging task.},
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In Information Retrieval evaluation, the classical approach of adopting binary relevance judgments has been replaced by multi-level relevance judgments and by gain-based metrics leveraging such multi-level judgment scales. Recent work has also proposed and evaluated unbounded relevance scales by means of Magnitude Estimation (ME) and compared them with multi-level scales. While ME brings advantages like the ability for assessors to always judge the next document as having higher or lower relevance than any of the documents they have judged so far, it also comes with some drawbacks. For example, it is not a natural approach for human assessors to judge items as they are used to do on the Web (e.g., 5-star rating). In this work, we propose and experimentally evaluate a bounded and fine-grained relevance scale having many of the advantages and dealing with some of the issues of ME. We collect relevance judgments over a 100-level relevance scale (S100) by means of a large-scale crowdsourcing experiment and compare the results with other relevance scales (binary, 4-level, and ME) showing the benefit of fine-grained scales over both coarse-grained and unbounded scales as well as highlighting some new results on ME. Our results show that S100 maintains the flexibility of unbounded scales like ME in providing assessors with ample choice when judging document relevance (i.e., assessors can fit relevance judgments in between of previously given judgments). It also allows assessors to judge on a more familiar scale (e.g., on 10 levels) and to perform efficiently since the very first judging task.
Mizzaro, Stefano; Mothe, Josiane; Roitero, Kevin; Ullah, Md Zia
Query Performance Prediction and Effectiveness Evaluation Without Relevance Judgments: Two Sides of the Same Coin Proceedings Article
In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1233–1236, Association for Computing Machinery, Ann Arbor, MI, USA, 2018, ISBN: 9781450356572.
@inproceedings{10.1145/3209978.3210146,
title = {Query Performance Prediction and Effectiveness Evaluation Without Relevance Judgments: Two Sides of the Same Coin},
author = {Stefano Mizzaro and Josiane Mothe and Kevin Roitero and Md Zia Ullah},
url = {https://doi.org/10.1145/3209978.3210146},
doi = {10.1145/3209978.3210146},
isbn = {9781450356572},
year = {2018},
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booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
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publisher = {Association for Computing Machinery},
address = {Ann Arbor, MI, USA},
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abstract = {Some methods have been developed for automatic effectiveness evaluation without relevance judgments. We propose to use those methods, and their combination based on a machine learning approach, for query performance prediction. Moreover, since predicting average precision as it is usually done in query performance prediction literature is sensitive to the reference system that is chosen, we focus on predicting the average of average precision values over several systems. Results of an extensive experimental evaluation on ten TREC collections show that our proposed methods outperform state-of-the-art query performance predictors.},
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Some methods have been developed for automatic effectiveness evaluation without relevance judgments. We propose to use those methods, and their combination based on a machine learning approach, for query performance prediction. Moreover, since predicting average precision as it is usually done in query performance prediction literature is sensitive to the reference system that is chosen, we focus on predicting the average of average precision values over several systems. Results of an extensive experimental evaluation on ten TREC collections show that our proposed methods outperform state-of-the-art query performance predictors.
Amigó, Enrique; Giner, Fernando; Mizzaro, Stefano; Spina, Damiano
A Formal Account of Effectiveness Evaluation and Ranking Fusion Proceedings Article
In: Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 123–130, Association for Computing Machinery, Tianjin, China, 2018, ISBN: 9781450356565.
@inproceedings{10.1145/3234944.3234958,
title = {A Formal Account of Effectiveness Evaluation and Ranking Fusion},
author = {Enrique Amigó and Fernando Giner and Stefano Mizzaro and Damiano Spina},
url = {https://doi.org/10.1145/3234944.3234958},
doi = {10.1145/3234944.3234958},
isbn = {9781450356565},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval},
pages = {123–130},
publisher = {Association for Computing Machinery},
address = {Tianjin, China},
series = {ICTIR '18},
abstract = {This paper proposes a theoretical framework which models the information provided by retrieval systems in terms of Information Theory. The proposed framework allows to formalize: (i) system effectiveness as an information theoretic similarity between system outputs and human assessments, and (ii) ranking fusion as an information quantity measure. As a result, the proposed effectiveness metric improves popular metrics in terms of formal constraints. In addition, our empirical experiments suggest that it captures quality aspects from traditional metrics, while the reverse is not true. Our work also advances the understanding of theoretical foundations of the empirically known phenomenon of effectiveness increase when combining retrieval system outputs in an unsupervised manner.},
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This paper proposes a theoretical framework which models the information provided by retrieval systems in terms of Information Theory. The proposed framework allows to formalize: (i) system effectiveness as an information theoretic similarity between system outputs and human assessments, and (ii) ranking fusion as an information quantity measure. As a result, the proposed effectiveness metric improves popular metrics in terms of formal constraints. In addition, our empirical experiments suggest that it captures quality aspects from traditional metrics, while the reverse is not true. Our work also advances the understanding of theoretical foundations of the empirically known phenomenon of effectiveness increase when combining retrieval system outputs in an unsupervised manner.
Amigó, Enrique; Fang, Hui; Mizzaro, Stefano; Zhai, ChengXiang
Are We on the Right Track? An Examination of Information Retrieval Methodologies Proceedings Article
In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 997–1000, Association for Computing Machinery, Ann Arbor, MI, USA, 2018, ISBN: 9781450356572.
@inproceedings{10.1145/3209978.3210131b,
title = {Are We on the Right Track? An Examination of Information Retrieval Methodologies},
author = {Enrique Amigó and Hui Fang and Stefano Mizzaro and ChengXiang Zhai},
url = {https://doi.org/10.1145/3209978.3210131},
doi = {10.1145/3209978.3210131},
isbn = {9781450356572},
year = {2018},
date = {2018-01-01},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {997–1000},
publisher = {Association for Computing Machinery},
address = {Ann Arbor, MI, USA},
series = {SIGIR '18},
abstract = {The unpredictability of user behavior and the need for effectiveness make it difficult to define a suitable research methodology for Information Retrieval (IR). In order to tackle this challenge, we categorize existing IR methodologies along two dimensions: (1) empirical vs. theoretical, and (2) top-down vs. bottom-up. The strengths and drawbacks of the resulting categories are characterized according to 6 desirable aspects. The analysis suggests that different methodologies are complementary and therefore, equally necessary. The categorization of the 167 full papers published in the last SIGIR (2016 and 2017) and ICTIR (2017) conferences suggest that most of existing work is empirical bottom-up, suggesting lack of some desirable aspects. With the hope of improving IR research practice, we propose a general methodology for IR that integrates the strengths of existing research methods.},
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The unpredictability of user behavior and the need for effectiveness make it difficult to define a suitable research methodology for Information Retrieval (IR). In order to tackle this challenge, we categorize existing IR methodologies along two dimensions: (1) empirical vs. theoretical, and (2) top-down vs. bottom-up. The strengths and drawbacks of the resulting categories are characterized according to 6 desirable aspects. The analysis suggests that different methodologies are complementary and therefore, equally necessary. The categorization of the 167 full papers published in the last SIGIR (2016 and 2017) and ICTIR (2017) conferences suggest that most of existing work is empirical bottom-up, suggesting lack of some desirable aspects. With the hope of improving IR research practice, we propose a general methodology for IR that integrates the strengths of existing research methods.
Amigó, Enrique; Fang, Hui; Mizzaro, Stefano; Zhai, ChengXiang
Report on the SIGIR 2017 Workshop on Axiomatic Thinking for Information Retrieval and Related Tasks (ATIR) Journal Article
In: SIGIR Forum, vol. 51, no 3, pp. 99–106, 2018, ISSN: 0163-5840.
@article{10.1145/3190580.3190596,
title = {Report on the SIGIR 2017 Workshop on Axiomatic Thinking for Information Retrieval and Related Tasks (ATIR)},
author = {Enrique Amigó and Hui Fang and Stefano Mizzaro and ChengXiang Zhai},
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abstract = {The SIGIR 2017 workshop on Axiomatic Thinking for Information Retrieval and Related Tasks took place on August 11, 2017 in Tokyo, Japan. The workshop aimed to help foster collaboration of researchers working on different perspectives of axiomatic thinking and encourage discussion and research on general methodological issues related to applying axiomatic thinking to information retrieval and related tasks. The program consisted of one keynote talk, four research presentations and a final panel discussion. This report outlines the events of the workshop and summarizes the major outcomes. More information about the workshop is available at https://www.eecis.udel.edu/~hfang/ATIR.html.},
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The SIGIR 2017 workshop on Axiomatic Thinking for Information Retrieval and Related Tasks took place on August 11, 2017 in Tokyo, Japan. The workshop aimed to help foster collaboration of researchers working on different perspectives of axiomatic thinking and encourage discussion and research on general methodological issues related to applying axiomatic thinking to information retrieval and related tasks. The program consisted of one keynote talk, four research presentations and a final panel discussion. This report outlines the events of the workshop and summarizes the major outcomes. More information about the workshop is available at https://www.eecis.udel.edu/~hfang/ATIR.html.
Roitero, Kevin; Passon, Marco; Serra, Giuseppe; Mizzaro, Stefano
Reproduce. Generalize. Extend. On Information Retrieval Evaluation without Relevance Judgments Journal Article
In: J. Data and Information Quality, vol. 10, no 3, 2018, ISSN: 1936-1955.
@article{10.1145/3241064,
title = {Reproduce. Generalize. Extend. On Information Retrieval Evaluation without Relevance Judgments},
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abstract = {The evaluation of retrieval effectiveness by means of test collections is a commonly used methodology in the information retrieval field. Some researchers have addressed the quite fascinating research question of whether it is possible to evaluate effectiveness completely automatically, without human relevance assessments. Since human relevance assessment is one of the main costs of building a test collection, both in human time and money resources, this rather ambitious goal would have a practical impact. In this article, we reproduce the main results on evaluating information retrieval systems without relevance judgments; furthermore, we generalize such previous work to analyze the effect of test collections, evaluation metrics, and pool depth. We also expand the idea to semi-automatic evaluation and estimation of topic difficulty. Our results show that (i) previous work is overall reproducible, although some specific results are not; (ii) collection, metric, and pool depth impact the automatic evaluation of systems, which is anyway accurate in several cases; (iii) semi-automatic evaluation is an effective methodology; and (iv) automatic evaluation can (to some extent) be used to predict topic difficulty.},
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The evaluation of retrieval effectiveness by means of test collections is a commonly used methodology in the information retrieval field. Some researchers have addressed the quite fascinating research question of whether it is possible to evaluate effectiveness completely automatically, without human relevance assessments. Since human relevance assessment is one of the main costs of building a test collection, both in human time and money resources, this rather ambitious goal would have a practical impact. In this article, we reproduce the main results on evaluating information retrieval systems without relevance judgments; furthermore, we generalize such previous work to analyze the effect of test collections, evaluation metrics, and pool depth. We also expand the idea to semi-automatic evaluation and estimation of topic difficulty. Our results show that (i) previous work is overall reproducible, although some specific results are not; (ii) collection, metric, and pool depth impact the automatic evaluation of systems, which is anyway accurate in several cases; (iii) semi-automatic evaluation is an effective methodology; and (iv) automatic evaluation can (to some extent) be used to predict topic difficulty.
Roitero, Kevin; Soprano, Michael; Mizzaro, Stefano
Effectiveness Evaluation with a Subset of Topics: A Practical Approach Proceedings Article
In: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). Ann Arbor Michigan, U.S.A, July 8-12, 2018. Conference Rank: GGS A++, Core A*, pp. 1145–1148, Association for Computing Machinery, Ann Arbor, MI, USA, 2018, ISBN: 9781450356572.
@inproceedings{conference-paper-sigir2018,
title = {Effectiveness Evaluation with a Subset of Topics: A Practical Approach},
author = {Kevin Roitero and Michael Soprano and Stefano Mizzaro},
url = {https://doi.org/10.1145/3209978.3210108},
doi = {10.1145/3209978.3210108},
isbn = {9781450356572},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). Ann Arbor Michigan, U.S.A, July 8-12, 2018. Conference Rank: GGS A++, Core A*},
pages = {1145–1148},
publisher = {Association for Computing Machinery},
address = {Ann Arbor, MI, USA},
series = {SIGIR '18},
abstract = {Several researchers have proposed to reduce the number of topics used in TREC-like initiatives. One research direction that has been pursued is what is the optimal topic subset of a given cardinality that evaluates the systems/runs in the most accurate way. Such a research direction has been so far mainly theoretical, with almost no indication on how to select the few good topics in practice. We propose such a practical criterion for topic selection: we rely on the methods for automatic system evaluation without relevance judgments, and by running some experiments on several TREC collections we show that the topics selected on the basis of those evaluations are indeed more informative than random topics.},
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Several researchers have proposed to reduce the number of topics used in TREC-like initiatives. One research direction that has been pursued is what is the optimal topic subset of a given cardinality that evaluates the systems/runs in the most accurate way. Such a research direction has been so far mainly theoretical, with almost no indication on how to select the few good topics in practice. We propose such a practical criterion for topic selection: we rely on the methods for automatic system evaluation without relevance judgments, and by running some experiments on several TREC collections we show that the topics selected on the basis of those evaluations are indeed more informative than random topics.
Maddalena, Eddy; Ceolin, Davide; Mizzaro, Stefano
Multidimensional News Quality: A Comparison of Crowdsourcing and Nichesourcing Proceedings Article
In: Cuzzocrea, Alfredo; Bonchi, Francesco; Gunopulos, Dimitrios (Ed.): Proceedings of the CIKM 2018 Workshops co-located with 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), Torino, Italy, October 22, 2018, CEUR-WS.org, 2018.
@inproceedings{DBLP:conf/cikm/MaddalenaCM18,
title = {Multidimensional News Quality: A Comparison of Crowdsourcing and
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2017
Checco, Alessandro; Roitero, Kevin; Maddalena, Eddy; Mizzaro, Stefano; Demartini, Gianluca
Let’s Agree to Disagree: Fixing Agreement Measures for Crowdsourcing Journal Article
In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, vol. 5, no 1, 2017.
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Roitero, Kevin; Mizzaro, Stefano; Serra, Giuseppe
Economic Evaluation of Recommender Systems: A Proposal Proceedings Article
In: Proceedings of the 8th Italian Information Retrieval Workshop, Lugano, Switzerland, June 05-07, 2017., pp. 48–51, 2017.
@inproceedings{DBLP:conf/iir/RoiteroMS17,
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Maddalena, Eddy; Roitero, Kevin; Demartini, Gianluca; Mizzaro, Stefano
Considering Assessor Agreement in IR Evaluation Proceedings Article
In: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, pp. 75–82, Association for Computing Machinery, New York, NY, USA, 2017, ISBN: 9781450344906.
@inproceedings{10.1145/3121050.3121060b,
title = {Considering Assessor Agreement in IR Evaluation},
author = {Eddy Maddalena and Kevin Roitero and Gianluca Demartini and Stefano Mizzaro},
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booktitle = {Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval},
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abstract = {The agreement between relevance assessors is an important but understudied topic in the Information Retrieval literature because of the limited data available about documents assessed by multiple judges. This issue has gained even more importance recently in light of crowdsourced relevance judgments, where it is customary to gather many relevance labels for each topic-document pair. In a crowdsourcing setting, agreement is often even used as a proxy for quality, although without any systematic verification of the conjecture that higher agreement corresponds to higher quality. In this paper we address this issue and we study in particular: the effect of topic on assessor agreement; the relationship between assessor agreement and judgment quality; the effect of agreement on ranking systems according to their effectiveness; and the definition of an agreement-aware effectiveness metric that does not discard information about multiple judgments for the same document as it typically happens in a crowdsourcing setting.},
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The agreement between relevance assessors is an important but understudied topic in the Information Retrieval literature because of the limited data available about documents assessed by multiple judges. This issue has gained even more importance recently in light of crowdsourced relevance judgments, where it is customary to gather many relevance labels for each topic-document pair. In a crowdsourcing setting, agreement is often even used as a proxy for quality, although without any systematic verification of the conjecture that higher agreement corresponds to higher quality. In this paper we address this issue and we study in particular: the effect of topic on assessor agreement; the relationship between assessor agreement and judgment quality; the effect of agreement on ranking systems according to their effectiveness; and the definition of an agreement-aware effectiveness metric that does not discard information about multiple judgments for the same document as it typically happens in a crowdsourcing setting.
Chifu, Adrian-Gabriel; Déjean, Sébastien; Mizzaro, Stefano; Mothe, Josiane
Human-Based Query Difficulty Prediction Proceedings Article
In: 39th European Colloquium on Information Retrieval (ECIR 2017), pp. pp. 343-356, Aberdeen, Scotland, United Kingdom, 2017.
@inproceedings{chifu:hal-01712541,
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Maddalena, Eddy; Mizzaro, Stefano; Scholer, Falk; Turpin, Andrew
On Crowdsourcing Relevance Magnitudes for Information Retrieval Evaluation Journal Article
In: ACM Trans. Inf. Syst., vol. 35, no 3, 2017, ISSN: 1046-8188.
@article{10.1145/3002172,
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publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Magnitude estimation is a psychophysical scaling technique for the measurement of sensation, where observers assign numbers to stimuli in response to their perceived intensity. We investigate the use of magnitude estimation for judging the relevance of documents for information retrieval evaluation, carrying out a large-scale user study across 18 TREC topics and collecting over 50,000 magnitude estimation judgments using crowdsourcing. Our analysis shows that magnitude estimation judgments can be reliably collected using crowdsourcing, are competitive in terms of assessor cost, and are, on average, rank-aligned with ordinal judgments made by expert relevance assessors.We explore the application of magnitude estimation for IR evaluation, calibrating two gain-based effectiveness metrics, nDCG and ERR, directly from user-reported perceptions of relevance. A comparison of TREC system effectiveness rankings based on binary, ordinal, and magnitude estimation relevance shows substantial variation; in particular, the top systems ranked using magnitude estimation and ordinal judgments differ substantially. Analysis of the magnitude estimation scores shows that this effect is due in part to varying perceptions of relevance: different users have different perceptions of the impact of relative differences in document relevance. These results have direct implications for IR evaluation, suggesting that current assumptions about a single view of relevance being sufficient to represent a population of users are unlikely to hold.},
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Magnitude estimation is a psychophysical scaling technique for the measurement of sensation, where observers assign numbers to stimuli in response to their perceived intensity. We investigate the use of magnitude estimation for judging the relevance of documents for information retrieval evaluation, carrying out a large-scale user study across 18 TREC topics and collecting over 50,000 magnitude estimation judgments using crowdsourcing. Our analysis shows that magnitude estimation judgments can be reliably collected using crowdsourcing, are competitive in terms of assessor cost, and are, on average, rank-aligned with ordinal judgments made by expert relevance assessors.We explore the application of magnitude estimation for IR evaluation, calibrating two gain-based effectiveness metrics, nDCG and ERR, directly from user-reported perceptions of relevance. A comparison of TREC system effectiveness rankings based on binary, ordinal, and magnitude estimation relevance shows substantial variation; in particular, the top systems ranked using magnitude estimation and ordinal judgments differ substantially. Analysis of the magnitude estimation scores shows that this effect is due in part to varying perceptions of relevance: different users have different perceptions of the impact of relative differences in document relevance. These results have direct implications for IR evaluation, suggesting that current assumptions about a single view of relevance being sufficient to represent a population of users are unlikely to hold.
Crestani, Fabio; Mizzaro, Stefano; Scagnetto, Ivan
MobileInformation Retrieval Book
1, 2017, ISBN: 978-3-319-60777-1.
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Pavan, Marco; Mizzaro, Stefano; Scagnetto, Ivan
Mining Movement Data to Extract Personal Points of Interest: A Feature Based Approach Book Chapter
In: Lai, Cristian; Giuliani, Alessandro; Semeraro, Giovanni (Ed.): Information Filtering and Retrieval: DART 2014: Revised and Invited Papers, pp. 35–61, Springer International Publishing, Cham, 2017, ISBN: 978-3-319-46135-9.
@inbook{Pavan2017,
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abstract = {Due to the widespread of mobile devices in recent years, records of the locations visited by users are common and growing, and the availability of such large amounts of spatio-temporal data opens new challenges to automatically discover valuable knowledge. One aspect that is being studied is the identification of important locations, i.e. places where people spend a fair amount of time during their daily activities; we address it with a novel approach. Our proposed method is organised in two phases: first, a set of candidate stay points is identified by exploiting some state-of-the-art algorithms to filter the GPS-logs; then, the candidate stay points are mapped onto a feature space having as dimensions the area underlying the stay point, its intensity (e.g. the time spent in a location) and its frequency (e.g. the number of total visits). We conjecture that the feature space allows to model aspects/measures that are more semantically related to users and better suited to reason about their similarities and differences than simpler physical measures (e.g. latitude, longitude, and timestamp). An experimental evaluation on the GeoLife public dataset confirms the effectiveness of our approach and sheds some light on the peculiar features and critical issues of location based systems.},
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Due to the widespread of mobile devices in recent years, records of the locations visited by users are common and growing, and the availability of such large amounts of spatio-temporal data opens new challenges to automatically discover valuable knowledge. One aspect that is being studied is the identification of important locations, i.e. places where people spend a fair amount of time during their daily activities; we address it with a novel approach. Our proposed method is organised in two phases: first, a set of candidate stay points is identified by exploiting some state-of-the-art algorithms to filter the GPS-logs; then, the candidate stay points are mapped onto a feature space having as dimensions the area underlying the stay point, its intensity (e.g. the time spent in a location) and its frequency (e.g. the number of total visits). We conjecture that the feature space allows to model aspects/measures that are more semantically related to users and better suited to reason about their similarities and differences than simpler physical measures (e.g. latitude, longitude, and timestamp). An experimental evaluation on the GeoLife public dataset confirms the effectiveness of our approach and sheds some light on the peculiar features and critical issues of location based systems.
2016
Roitero, Kevin; Mizzaro, Stefano
Improving the Efficiency of Retrieval Effectiveness Evaluation: Finding a Few Good Topics with Clustering? Proceedings Article
In: Proceedings of the 7th Italian Information Retrieval Workshop, Venezia, Italy, May 30-31, 2016., 2016.
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Pavan, Marco; Mizzaro, Stefano; Bernardon, Matteo; Scagnetto, Ivan
Exploiting News to Categorize Tweets: Quantifying the Impact of Different News Collections Proceedings Article
In: -, Miguel Martinez; Kruschwitz, Udo; Kazai, Gabriella; Hopfgartner, Frank; Corney, David; Campos, Ricardo; Albakour, Dyaa (Ed.): Proceedings of the First International Workshop on Recent Trends in News Information Retrieval co-located with 38th European Conference on Information Retrieval (ECIR 2016), Padua, Italy, March 20, 2016, pp. 54–59, CEUR-WS.org, 2016.
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2015
Pavan, M; Mizzaro, S; Scagnetto, I; Beggiato, A
Finding Important Locations: A Feature-Based Approach Proceedings Article
In: 2015 16th IEEE International Conference on Mobile Data Management, pp. 110-115, 2015.
@inproceedings{7264310b,
title = {Finding Important Locations: A Feature-Based Approach},
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Mizzaro, Stefano; Pavan, Marco; Scagnetto, Ivan
Content-Based Similarity of Twitter Users Proceedings Article
In: Hanbury, Allan; Kazai, Gabriella; Rauber, Andreas; Fuhr, Norbert (Ed.): Advances in Information Retrieval, pp. 507–512, Springer International Publishing, Cham, 2015, ISBN: 978-3-319-16354-3.
@inproceedings{10.1007/978-3-319-16354-3_56,
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year = {2015},
date = {2015-01-01},
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abstract = {We propose a method for computing user similarity based on a network representing the semantic relationships between the words occurring in the same tweet and the related topics. We use such specially crafted network to define several user profiles to be compared with cosine similarity. We also describe an initial experimental activity to study the effectiveness on a limited dataset.},
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We propose a method for computing user similarity based on a network representing the semantic relationships between the words occurring in the same tweet and the related topics. We use such specially crafted network to define several user profiles to be compared with cosine similarity. We also describe an initial experimental activity to study the effectiveness on a limited dataset.
2011
Mizzaro, Stefano; Vassena, Luca
A social approach to context-aware retrieval Journal Article
In: World Wide Web, vol. 14, no 4, pp. 377-405, 2011, ISSN: 1573-1413.
@article{Mizzaro2011,
title = {A social approach to context-aware retrieval},
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abstract = {We present a general purpose solution to Web content and services perusal by means of mobile devices, named Social Context-Aware Browser. This is a novel approach for information access based on users' context, that exploits social and collaborative models to overtake the limits of the existing solutions. Instead of relying on a pool of experts and on a rigid categorization, as it is usually done in the context-aware field, our solution allows the crowd of users to model, control, and manage the contextual knowledge through collaboration and participation. To have a dynamic and user-tailored context representation, and to enhance the process of retrieval based on users' actual situation, the community of users is encouraged to define the contexts of interest, to share, use, and discuss them, and to associate context to content and resources (Web pages, services, applications, etc.). This paper provides an overall presentation of our solution, describing the idea, the implementation, and the evaluation through a benchmark based methodology.},
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We present a general purpose solution to Web content and services perusal by means of mobile devices, named Social Context-Aware Browser. This is a novel approach for information access based on users' context, that exploits social and collaborative models to overtake the limits of the existing solutions. Instead of relying on a pool of experts and on a rigid categorization, as it is usually done in the context-aware field, our solution allows the crowd of users to model, control, and manage the contextual knowledge through collaboration and participation. To have a dynamic and user-tailored context representation, and to enhance the process of retrieval based on users' actual situation, the community of users is encouraged to define the contexts of interest, to share, use, and discuss them, and to associate context to content and resources (Web pages, services, applications, etc.). This paper provides an overall presentation of our solution, describing the idea, the implementation, and the evaluation through a benchmark based methodology.
2010
Coppola, P; Mea, Della V; Gaspero, Di L; Menegon, D; Mischis, D; Mizzaro, S; Scagnetto, I; Vassena, L
The Context-Aware Browser Journal Article
In: IEEE Intelligent Systems, vol. 25, no 1, pp. 38-47, 2010.
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Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Lomuscio, Raffaella; Mischis, Danny; Mizzaro, Stefano; Nazzi, Elena; Scagnetto, Ivan; Vassena, Luca
AI Techniques in a Context-Aware Ubiquitous Environment Book Chapter
In: Hassanien, Aboul-Ella; Abawajy, Jemal H; Abraham, Ajith; Hagras, Hani (Ed.): Pervasive Computing: Innovations in Intelligent Multimedia and Applications, pp. 157–180, Springer London, London, 2010, ISBN: 978-1-84882-599-4.
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abstract = {Nowadays, the mobile computing paradigm and the widespread diffusion of mobile devices are quickly changing and replacing many common assumptions about software architectures and interaction/communication models. The environment, in particular, or more generally, the so-called user context is claiming a central role in everyday's use of cellular phones, PDAs, etc. This is due to the huge amount of data ``suggested'' by the surrounding environment that can be helpful in many common tasks. For instance, the current context can help a search engine to refine the set of results in a useful way, providing the user with a more suitable and exploitable information. Moreover, we can take full advantage of this new data source by ``pushing'' active contents towards mobile devices, empowering the latter with new features (e.g., applications) that can allow the user to fruitfully interact with the current context. Following this vision, mobile devices become dynamic self-adapting tools, according to the user needs and the possibilities offered by the environment. The present work proposes MoBe: an approach for providing a basic infrastructure for pervasive context-aware applications on mobile devices, in which AI techniques (namely a principled combination of rule-based systems, Bayesian networks and ontologies) are applied to context inference. The aim is to devise a general inferential framework to make easier the development of context-aware applications by integrating the information coming from physical and logical sensors (e.g., position, agenda) and reasoning about this information in order to infer new and more abstract contexts.},
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Nowadays, the mobile computing paradigm and the widespread diffusion of mobile devices are quickly changing and replacing many common assumptions about software architectures and interaction/communication models. The environment, in particular, or more generally, the so-called user context is claiming a central role in everyday's use of cellular phones, PDAs, etc. This is due to the huge amount of data ``suggested'' by the surrounding environment that can be helpful in many common tasks. For instance, the current context can help a search engine to refine the set of results in a useful way, providing the user with a more suitable and exploitable information. Moreover, we can take full advantage of this new data source by ``pushing'' active contents towards mobile devices, empowering the latter with new features (e.g., applications) that can allow the user to fruitfully interact with the current context. Following this vision, mobile devices become dynamic self-adapting tools, according to the user needs and the possibilities offered by the environment. The present work proposes MoBe: an approach for providing a basic infrastructure for pervasive context-aware applications on mobile devices, in which AI techniques (namely a principled combination of rule-based systems, Bayesian networks and ontologies) are applied to context inference. The aim is to devise a general inferential framework to make easier the development of context-aware applications by integrating the information coming from physical and logical sensors (e.g., position, agenda) and reasoning about this information in order to infer new and more abstract contexts.
2009
Carpineto, Claudio; Mizzaro, Stefano; Romano, Giovanni; Snidero, Matteo
Mobile information retrieval with search results clustering: Prototypes and evaluations Journal Article
In: JASIST, vol. 60, no 5, pp. 877–895, 2009.
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Menegon, Davide; Mizzaro, Stefano; Nazzi, Elena; Vassena, Luca
Evaluating Mobile Proactive Context-Aware Retrieval: An Incremental Benchmark Proceedings Article
In: Azzopardi, Leif; Kazai, Gabriella; Robertson, Stephen; Rüger, Stefan; Shokouhi, Milad; Song, Dawei; Yilmaz, Emine (Ed.): Advances in Information Retrieval Theory, pp. 362–365, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, ISBN: 978-3-642-04417-5.
@inproceedings{10.1007/978-3-642-04417-5_40,
title = {Evaluating Mobile Proactive Context-Aware Retrieval: An Incremental Benchmark},
author = {Davide Menegon and Stefano Mizzaro and Elena Nazzi and Luca Vassena},
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We present the evaluation of a novel application for Web content perusal by means of context-aware mobile devices that proactively query an external search engine. To this aim, we develop a TREC-like benchmark and we use it to evaluate different strategies for automatic query construction on the basis of user's current context. We discuss both the methodology and the results.
Mizzaro, Stefano; Nazzi, Elena; Vassena, Luca
Collaborative Annotation for Context-Aware Retrieval Proceedings Article
In: Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval, pp. 42–45, Association for Computing Machinery, Barcelona, Spain, 2009, ISBN: 9781605584300.
@inproceedings{10.1145/1506250.1506260,
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We discuss how collaborative annotations can be exploited to simplify and improve the management of context and resources in the context-aware retrieval field. We apply this approach to our Context Aware Browser, a general purpose solution to Web content perusal by means of mobile devices, based on the user's context. Instead of relying on a pool of experts and on a rigid categorization, as it is usually done in the context-aware field, our solution allows the crowd of users to model, control and manage the contextual knowledge through collaboration and participation. We propose two models and we outline an example of application.
Sabbata, Stefano De; Mizzaro, Stefano; Vassena, Luca
Where do you Roll Today? Trajectory Prediction by SpaceRank and Physics Models Book Chapter
In: Gartner, Georg; Rehrl, Karl (Ed.): Location Based Services and TeleCartography II: From Sensor Fusion to Context Models, pp. 63–78, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, ISBN: 978-3-540-87393-8.
@inbook{Sabbata2009,
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Pre-destination, the prediction of a user's future destination, is recently gaining interest and importance in location-aware, ubiquitous, and mobile computing. An increasing amount of data related to position of people is becoming available because people usually take their mobile devices (phones, smart-phones, PDAs, etc.) with them. We propose to mine these data to derive the importance of the single locations in an area of interest, given by either a single user or a community. Then we use the importance of locations as basis for our approach to pre-destination, where well-known physics models (namely gravitation and electrical force) are exploited to estimate users trajectories and future destinations.
Mizzaro, Stefano; Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Mischis, Danny; Nazzi, Elena; Scagnetto, Ivan; Vassena, Luca
Context Aware Browser Proceedings Article
In: Belkin, Nicholas J; Fuhr, Norbert; Jose, Joemon; van Rijsbergen, Keith C J (Ed.): Interactive Information Retrieval, Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany, Dagstuhl, Germany, 2009, ISSN: 1862-4405.
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2008
Mizzaro, Stefano; Nazzi, Elena; Vassena, Luca
Retrieval of Context-Aware Applications on Mobile Devices: How to Evaluate? Proceedings Article
In: Proceedings of the Second International Symposium on Information Interaction in Context, pp. 65–71, Association for Computing Machinery, London, United Kingdom, 2008, ISBN: 9781605583105.
@inproceedings{10.1145/1414694.1414710,
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publisher = {Association for Computing Machinery},
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abstract = {This paper discusses the issue of evaluation of context-aware retrieval applications. We begin by describing MoBe, a specific architecture that allows automatic download and execution of context-aware applications on mobile devices. In MoBe, the most relevant applications are selected by matching context and application descriptors. Since several alternatives for descriptors implementation exist, it is important to compare their effectiveness. To this aim, we develop a TREC-like benchmark, in which the collection is made up by a set of application descriptors and the topics are made up by context descriptors. We then use the benchmark to evaluate the effectiveness of different descriptors components, and of structured and unstructured (i.e., free text) data, finding results that are somehow useful for future MoBe development. We also discuss the issue of the evaluation methodology for highly interactive and novel applications like context-aware retrieval systems, and MoBe in particular.},
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This paper discusses the issue of evaluation of context-aware retrieval applications. We begin by describing MoBe, a specific architecture that allows automatic download and execution of context-aware applications on mobile devices. In MoBe, the most relevant applications are selected by matching context and application descriptors. Since several alternatives for descriptors implementation exist, it is important to compare their effectiveness. To this aim, we develop a TREC-like benchmark, in which the collection is made up by a set of application descriptors and the topics are made up by context descriptors. We then use the benchmark to evaluate the effectiveness of different descriptors components, and of structured and unstructured (i.e., free text) data, finding results that are somehow useful for future MoBe development. We also discuss the issue of the evaluation methodology for highly interactive and novel applications like context-aware retrieval systems, and MoBe in particular.
Bulfoni, Adolfo; Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Mischis, Danny; Mizzaro, Stefano; Scagnetto, Ivan; Vassena, Luca
AI on the Move: Exploiting AI Techniques for Context Inference on Mobile Devices Proceedings Article
In: Proceedings of the 2008 Conference on ECAI 2008: 18th European Conference on Artificial Intelligence, pp. 668–672, IOS Press, NLD, 2008, ISBN: 9781586038915.
@inproceedings{10.5555/1567281.1567426,
title = {AI on the Move: Exploiting AI Techniques for Context Inference on Mobile Devices},
author = {Adolfo Bulfoni and Paolo Coppola and Vincenzo Della Mea and Luca Di Gaspero and Danny Mischis and Stefano Mizzaro and Ivan Scagnetto and Luca Vassena},
isbn = {9781586038915},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the 2008 Conference on ECAI 2008: 18th European Conference on Artificial Intelligence},
pages = {668–672},
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abstract = {Context aware computing is a computational paradigm that has faced a rapid growth in the last few years, especially in the field of mobile devices. One of the promises of context-awareness in this field is the possibility of automatically adapting the functioning mode of mobile devices to the environment and the current situation the user is in, with the aim of improving both their efficiency (using the scarce resources in a more efficient way) and effectiveness (providing better services to the user). We propose a novel approach for providing a basic infrastructure for context-aware applications on mobile devices, in which AI techniques (namely a principled combination of rule-based systems, Bayesian networks, and ontologies) are applied to context inference. The aim is to devise a general inferential framework to easier the development of context-aware applications by integrating the information coming from physical and logical sensors (e.g., position, agenda) and reasoning about this information in order to infer new and more abstract contexts. In previous contextaware applications, most researches focused almost exclusively on time and/or location and other few data, while the same contexts inference was limited to preconceived values. Our approach differs from previous works since we do not focus on particular contextual values, but rather we have developed an architecture where managed contexts can be easily replaced by new contexts, depending on the different needs. Moreover, the inferential infrastructure we designed is able to work in a more general way and can be easily adapted to different models of applications distribution. We show some concrete examples of applications built upon the inferential infrastructure and we discuss its strengths and limitations.},
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Context aware computing is a computational paradigm that has faced a rapid growth in the last few years, especially in the field of mobile devices. One of the promises of context-awareness in this field is the possibility of automatically adapting the functioning mode of mobile devices to the environment and the current situation the user is in, with the aim of improving both their efficiency (using the scarce resources in a more efficient way) and effectiveness (providing better services to the user). We propose a novel approach for providing a basic infrastructure for context-aware applications on mobile devices, in which AI techniques (namely a principled combination of rule-based systems, Bayesian networks, and ontologies) are applied to context inference. The aim is to devise a general inferential framework to easier the development of context-aware applications by integrating the information coming from physical and logical sensors (e.g., position, agenda) and reasoning about this information in order to infer new and more abstract contexts. In previous contextaware applications, most researches focused almost exclusively on time and/or location and other few data, while the same contexts inference was limited to preconceived values. Our approach differs from previous works since we do not focus on particular contextual values, but rather we have developed an architecture where managed contexts can be easily replaced by new contexts, depending on the different needs. Moreover, the inferential infrastructure we designed is able to work in a more general way and can be easily adapted to different models of applications distribution. We show some concrete examples of applications built upon the inferential infrastructure and we discuss its strengths and limitations.
Coppola, Paolo; Lomuscio, Raffaella; Mizzaro, Stefano; Nazzi, Elena; Vassena, Luca
Mobile Social Software for Cultural Heritage: A Reference Model Proceedings Article
In: Flejter, Dominik; Grzonkowski, Slawomir; Kaczmarek, Tomasz; Kowalkiewicz, Marek; Nagle, Tadhg; Parkes, Jonny (Ed.): BIS 2008 Workshops Proceedings: Social Aspects of the Web (SAW 2008), Advances in Accessing Deep Web (ADW 2008), E-Learning for Business Needs, Innsbruck, Austria, 6-7 May 2008, pp. 69–80, CEUR-WS.org, 2008.
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Coppola, Paolo; Gaspero, Luca Di; Lomuscio, Raffaella; Mischis, Danny; Mizzaro, Stefano; Nazzi, Elena; Scagnetto, Ivan; Vassena, Luca
AI Techniques in a context-aware ubiquitous environment Book Chapter
In: Hassanien, Aboul Ella; Abraham, Ajith; Hagras, Hani (Ed.): Pervasive Computing: Innovations in Intelligent Multimedia and Applications, pp. 157–180, Springer, London, United Kingdom, 2008, ISBN: 978-1-84882-598-7.
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2007
Mea, Vincenzo Della; Gaspero, Luca Di; Scagnetto, Ivan
Programmazione Web Lato Server: Principi, Tecniche di base, Esercitazioni Book
Apogeo, 2007, ISBN: 978-88-503-2610-5.
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2006
Mea, Vincenzo Della; Demartini, Gianluca; Gaspero, Luca Di; Mizzaro, Stefano
Experiments on Average Distance Measure Book Section
In: Lalmas, Mounia; MacFarlane, Andy; Rüger, Stefan M; Tombros, Anastasios; Tsikrika, Theodora; Yavlinsky, Alexei (Ed.): Advances in Information Retrieval, 28th European Conference on IR Research (ECIR 2006), vol. 3936, pp. 492–495, Springer Verlag, Berlin-Heidelberg, Germany, 2006.
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Mea, Vincenzo Della; Demartini, Gianluca; Gaspero, Luca Di; Mizzaro, Stefano
Measuring Retrieval Effectiveness with Average Distance Measure (ADM) Journal Article
In: Information Wissenschaft und Praxis, vol. 57, no 8, pp. 405–416, 2006, ISSN: 1434-4653.
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2005
Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Mizzaro, Stefano; Scagnetto, Ivan; Selva, Andrea; Vassena, Luca; Riziò, Paolo Zandegiacomo
Information Filtering and Retrieving of Context-Aware Applications Within the MoBe Framework Proceedings Article
In: Proceedings International Workshop on Context-Based Information Retrieval (CIR-2005), 2005.
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Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Mizzaro, Stefano; Scagnetto, Ivan; Selva, Andrea; Vassena, Luca; Riziò, Paolo Zandegiacomo
MoBe: A Framework for Context-Aware Mobile Applications Proceedings Article
In: Floréen, Patrik; Lindń, Greger; Niklander, Tiina; Raatikainen, Kimmo (Ed.): Proceedings of the Workshop on Context Awareness for Proactive Systems (CAPS 2005), pp. 55–66, Helsinki University Press, Helsinki, Finland, 2005.
@inproceedings{CDDMSSVZ05ab,
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Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Mizzaro, Stefano; Scagnetto, Ivan; Selva, Andrea; Vassena, Luca; Riziò, Paolo Zandegiacomo
Context-Aware Mobile Applications on Mobile Devices for Mobile Users Proceedings Article
In: Proceedings of the 1st International Workshop on Exploiting Context Histories in Smart Environments, Munich, Germany, 2005.
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Mea, Vincenzo Della; Gaspero, Luca Di; Mizzaro, Stefano; Scagnetto, Ivan; Selva, Andrea; Vassena, Luca; Z, Paolo; Riziò, Giacomo
MoBe: Context-Aware Mobile Applications on Mobile Devices for Mobile Users Proceedings Article
In: Press, Helsinki University (Ed.): Workshop on Context Awareness for Proactive Systems, CAPS 2005, ISBN: 952-10-2518.
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2004
Mea, Vincenzo Della; Gaspero, Luca Di; Mizzaro, Stefano
Evaluating ADM on a four-level relevance scale document set from NTCIR Proceedings Article
In: Proceedings of NTCIR Workshop 4 Meeting - Supplement, National Institute of Informatics (NII), Tokyo, Japan, 2004.
@inproceedings{DeDM04,
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booktitle = {Proceedings of NTCIR Workshop 4 Meeting - Supplement},
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Gaspero, Luca Di; Mizzaro, Stefano; Schaerf, Andrea
A MultiAgent Architecture for Distributed Course Timetabling Proceedings Article
In: Atti della Giornata di Lavoro del gruppo RCRA 2004: Agenti e Vincoli: Modelli e Tecnologie per Dominare la Complessità, 2004, (Available as electronic proceedings).
@inproceedings{DiMS04,
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2003
Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Mizzaro, Stefano
The Concept of Relevance in Mobile and Ubiquitous Information Access Book Section
In: Crestani, Fabrizio; Dunlop, Mark; Mizzaro, Stefano (Ed.): Mobile and Ubiquitous Information Access, vol. 2954, pp. 1–10, Springer Verlag, Berlin-Heidelberg, Germany, 2003, ISBN: 978-3-540-21003-0.
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2002
Coppola, Paolo; Mea, Vincenzo Della; Gaspero, Luca Di; Mizzaro, Stefano; Ranon, Roberto
From E-Relevance to W-Relevance Proceedings Article
In: Leong, M -K; Loudon, G (Ed.): Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 64–72, Tampere, Finland, 2002.
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1996
Brajnik, Giorgio; Mizzaro, Stefano; Tasso, Carlo
Evaluating User Interfaces to Information Retrieval Systems: A Case Study on User Support Proceedings Article
In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum), pp. 128–136, 1996.
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title = {Evaluating User Interfaces to Information Retrieval Systems: A Case
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