BIGFile: Bayesian Information Gain for Fast File Retrieval

Wanyu Liu (1, 2) , Olivier Rioul (3, 4) , Joanna Mcgrenere (5, 2) , Wendy Mackay (2) , Michel Beaudouin-Lafon (2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 EX-SITU - Extreme Interaction
3 COMNUM - Communications Numériques
4 COMELEC - Département Communications & Electronique
5 UBC Computer Science - Department of Computer Science [Vancouver]

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.

Mots clés: ACM Classification Keywords H52 [Information Interfaces and Presentation]: User Interfaces-Interaction styles Author Keywords Navigation-based file retrieval; Split adaptive interfaces; Bayesian approach; Mutual information

Domaines: Informatique [cs]; Interface homme-machine [cs.HC]

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⟩