BIGFile: Bayesian Information Gain for Fast File Retrieval - Archive ouverte HAL
Communication Dans Un Congrès Année : 2018

BIGFile: Bayesian Information Gain for Fast File Retrieval

Résumé

We introduce BIGFile, a new fast file retrieval technique based on the Bayesian Information Gain framework. BIGFile provides interface shortcuts to assist the user in navigating to a desired target (file or folder). BIGFile's split interface combines a traditional list view with an adaptive area that displays shortcuts to the set of file paths estimated by our computa-tionally efficient algorithm. Users can navigate the list as usual, or select any part of the paths in the adaptive area. A pilot study of 15 users informed the design of BIGFile, revealing the size and structure of their file systems and their file retrieval practices. Our simulations show that BIGFile outper-forms Fitchett et al.'s AccessRank, a best-of-breed prediction algorithm. We conducted an experiment to compare BIGFile with ARFile (AccessRank instantiated in a split interface) and with a Finder-like list view as baseline. BIGFile was by far the most efficient technique (up to 44% faster than ARFile and 64% faster than Finder), and participants unanimously preferred the split interfaces to the Finder.
Fichier principal
Vignette du fichier
BIGFile.pdf (1.47 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01791754 , version 1 (14-05-2018)

Identifiants

Citer

Wanyu Liu, Olivier Rioul, Joanna Mcgrenere, Wendy Mackay, Michel Beaudouin-Lafon. BIGFile: Bayesian Information Gain for Fast File Retrieval. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18), Apr 2018, Montreal QC, Canada. ⟨10.1145/3173574.3173959⟩. ⟨hal-01791754⟩
608 Consultations
550 Téléchargements

Altmetric

Partager

More