2016/02/16 by Gangli Liu, Liu, Gangli
Computer Science · Decision Sciences · Neuroscience · #Advanced Database Systems and Queries #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #H.3.3 #Information Retrieval (cs.IR) #Personal Information Management and User Behavior #cs.IR
paper · pdf · doi:10.48550/arxiv.1602.05157
openalex publication_date 2016/02/16 · arxiv created 2016/02/18 · arxiv updated 2016/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 artificially based on the user's own document corpus.