Search result diversification in resource selection for federated search

Abstract

Prior research in resource selection for federated search mainly focused on selecting a small number of information sources that are most relevant to a user query. However, result novelty and diversification are largely unexplored, which does not reflect the various kinds of information needs of users in real world applications. This paper proposes two general approaches to model both result relevance and diversification in selecting sources, in order to provide more comprehensive coverage of multiple aspects of a user query. The first approach focuses on diversifying the document ranking on a centralized sample database before selecting information sources under the framework of Relevant Document Distribution Estimation (ReDDE). The second approach first evaluates the relevance of information sources with respect to each aspect of the query, and then ranks the sources based on the novelty and relevance that they offer. Both approaches can be applied with a wide range of existing resource selection algorithms such as ReDDE, CRCS, CORI and Big Document. Moreover, this paper proposes a learning based approach to combine multiple resource selection algorithms for result diversification, which can further improve the performance. We propose a set of new metrics for resource selection in federated search to evaluate the diversification performance of different approaches. To our best knowledge, this is the first piece of work that addresses the problem of search result diversification in federated search. The effectiveness of the proposed approaches has been demonstrated by an extensive set of experiments on the federated search testbed of the Clueweb dataset.