Re-ranking search results using language models of query-specific clusters

To obtain high precision at top ranks by a search performed in response to a query, researchers have proposed a cluster-based re-ranking paradigm: clustering an initial list of documents that are the most highly ranked by some initial search, and using information induced from these (often called) query-specific clusters for re-ranking the list. However, results concerning the effectiveness of various automatic cluster-based re-ranking methods have been inconclusive. We show that using query-specific clusters for automatic re-ranking of top-retrieved documents is effective with several methods in which clusters play different roles, among which is the smoothing of document language models. We do so by adapting previously-proposed cluster-based retrieval approaches, which are based on (static) query-independent clusters for ranking all documents in a corpus, to the re-ranking setting wherein clusters are query-specific. The best performing method that we develop outperforms both the initial document-based ranking and some previously proposed cluster-based re-ranking approaches; furthermore, this algorithm consistently outperforms a state-of-the-art pseudo-feedback-based approach. In further exploration we study the performance of cluster-based smoothing methods for re-ranking with various (soft and hard) clustering algorithms, and demonstrate the importance of clusters in providing context from the initial list through a comparison to using single documents to this end.