How Random Decisions Affect Selective Distributed Search

Abstract

Selective distributed search is a retrieval architecture that reduces search costs by partitioning a corpus into topical shards such that only a few shards need to be searched for each query. Prior research created topical shards by using random seed documents to cluster a random sample of the full corpus. The resource selection algorithm might use a different random sample of the corpus. These random components make selective search non-deterministic. This paper studies how these random components affect experimental results. Experiments on two ClueWeb09 corpora and four query sets show that in spite of random components, selective search is stable for most queries.