ReFinder: A Context-Based Information Re-Finding System
In this paper, we present a context-based information re-finding system called ReFinder. It leverages human's natural recall characteristics and allows users to re-find files and Web pages according to the previous access context. ReFinder re-finds information based on a query-by-context model over a context memory snapshot, linking to the accessed information contents. Context instances in the memory snapshot are organized in a clustered and associated manner, and dynamically evolve in life cycles to mimic brain memory's decay and reinforcement phenomena. We evaluate the scalability of ReFinder on a large synthetic data set. The experimental results show that consistent degradation of context instances in the context memory and the ones in user's re-finding requests can lead to the best re-finding precision and recall. An 8-week user study is also conducted to examine the applicability of ReFinder. Initial findings show that time, place and activity could serve as useful recall clues. On average, 15.53 seconds are needed to complete a re-finding request with ReFinder and 84.42 seconds with other existing methods. Some further possible improvement of ReFinder is also discussed at the end of the paper.