Novel applications such as recommender systems, uncertain databases, and multimedia databases are designed to process similarity queries that produce ranked lists of objects as their results. Similarity queries typically result in disk access latency and incur a substantial computational cost. In this paper, we propose an 'active caching' technique for similarity queries that is capable of synthesizing query results from cached information even when the required result list is not explicitly stored in the cache. Our solution, the Cache Estimated Significance (CES) model, is based on shared-neighbor similarity measures, which assess the strength of the relationship between two objects as a function of the number of other objects in the common intersection of their neighborhoods. The proposed method is general in that it does not require that the features be drawn from a metric space, nor does it require that the partial orders induced by the similarity measure be monotonic. Experimental results on real data sets show a substantial cache hit rate when compared with traditional caching approaches.
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