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In this paper, we present a context-based information refinding system called ReFinder. It leverages human's natural recall characteristics and allows users to refind files and Web pages according to the previous access context. ReFinder refinds 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 refinding requests can lead to the best refinding 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 refinding 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.