A Ranking Algorithm for Re-finding

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 artificially based on the user's own document corpus.