Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSelecting the top-quality item through crowd scoring
Anno di pubblicazione2018
  • Elettronico
  • Cartaceo
Autore/iAlessandro Nordio, Alberto Tarable, Emilio Leonardi, Marco Ajmone Marsan
Affiliazioni autoriAlessandro Nordio , CNR-IEIIT, Torino, Italy Alberto Tarable , CNR-IEIIT, Torino, Italy Emilio Leonardi , Politecnico di Torino and CNR-IEIIT, Torino, Italy Marco Ajmone Marsan , Politecnico di Torino and CNR-IEIIT, Torino, Italy; IMDEA, Madrid, Spain
Autori CNR e affiliazioni
  • inglese
AbstractWe investigate crowdsourcing algorithms for finding the top-quality item within a large collection of objects with unknown intrinsic quality values. This is an important problem with many relevant applications, for example in networked recommendation systems. The core of the algorithms is that objects are distributed to crowd workers, who return a noisy and biased evaluation. All received evaluations are then combined, to identify the top-quality object. We first present a simple probabilistic model for the system under investiga- tion. Then, we devise and study a class of efficient adaptive algorithms to assign in an effective way objects to workers. We compare the performance of several algorithms, which correspond to different choices of the design parameters/metrics. In the simulations we show that some of the algorithms achieve near optimal performance for a suitable setting of the system parameters.
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RivistaACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)
ISSN: 2376-3639
Titolo chiave: ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)
Numero volume della rivista-
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Verificato da refereeSì: Internazionale
Stato della pubblicazionePostprint
Indicizzazione (in banche dati controllate)-
Parole chiaveCrowd scoring
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Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR
    Progetti Europei-