HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

BIGFile: Bayesian Information Gain for Fast File Retrieval

Abstract : 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.
Complete list of metadata

Cited literature [40 references]  Display  Hide  Download

Contributor : Wanyu Liu Connect in order to contact the contributor
Submitted on : Monday, May 14, 2018 - 6:10:51 PM
Last modification on : Wednesday, November 3, 2021 - 8:16:45 AM
Long-term archiving on: : Tuesday, September 25, 2018 - 2:19:59 AM


Files produced by the author(s)



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⟩



Record views


Files downloads