
TL;DR
This paper introduces a novel ranking algorithm that improves re-finding files on personal computers by using user responses to targeted questions and assessing familiarity, especially for difficult cases.
Contribution
It presents a new method combining question-based filtering and familiarity assessment to enhance re-finding accuracy, including an approach to generate artificial re-finding tasks.
Findings
Effective ranking of candidate files based on user answers.
Improved success rate in difficult re-finding tasks.
Method for generating artificial re-finding tasks.
Abstract
Re-finding files from a personal computer is a frequent demand to users. When encountered a difficult re-finding task, people may not recall the attributes used by conventional re-finding methods, such as a file's path, file name, keywords etc., the re-finding would fail. We proposed a method to support difficult re-finding tasks. By asking the user a list of questions about the target, such as a document's pages, author numbers, accumulated reading time, last reading location etc. Then use the user's answers to filter out the target. After the user answered a list of questions about the target file, we evaluate the user's familiar degree about the target file based on the answers. We devise a ranking algorithm which sorts the candidates by comparing the user's familiarity degree about the target and the candidates. We also propose a method to generate re-finding tasks…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPersonal Information Management and User Behavior · EEG and Brain-Computer Interfaces · Advanced Database Systems and Queries
