Partitioning and Segment Organization Strategies for Real-Time Selective Search on Document Streams

Abstract

The basic idea behind selective search is to partition a collection into topical clusters, and for each query, consider only a subset of the clusters that are likely to contain relevant documents. Previous work on web collections has shown that it is possible to retain high-quality results while considering only a small fraction of the collection. These studies, however, assume static collections where it is feasible to run batch clustering algorithms for partitioning. In this work, we consider the novel formulation of selective search on document streams (specifically, tweets), where partitioning must be performed incrementally. In our approach, documents are partitioned into temporal segments and selective search is performed within each segment: these segments can either be clustered using batch or online algorithms, and at different temporal granularities. For efficiency, we take advantage of word embeddings to reduce the dimensionality of the document vectors. Experiments with test collections from the TREC Microblog Tracks show that we are able to achieve precision indistinguishable from exhaustive search while considering only around 5% of the collection. Interestingly, we observe no significant effectiveness differences between batch vs. online clustering and between hourly vs. daily temporal segments, despite them being very different index organizations. This suggests that architectural choices should be primarily guided by efficiency considerations.