A Diagnostic Study of Search Result Diversification Methods

  • Authors:

  • Wei Zheng;Hui Fang

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Delaware, Newark, DE USA;Department of Electrical and Computer Engineering, University of Delaware, Newark, DE USA

  • Venue:
  • Proceedings of the 2013 Conference on the Theory of Information Retrieval
  • Year:
  • 2013

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Abstract

Search result diversification aims to maximize the coverage of different pieces of relevant information in the search results. Many diversification methods have been proposed and studied. However, the advantage and disadvantage of each method still remain unclear. In this paper, we conduct a diagnostic study over two state of the art diversification methods with the goal of identifying the weaknesses of these methods to further improve the performance. Specifically, we design a set of perturbation tests that isolate individual factors, i.e., relevance and diversity, which affect the diversification performance. The test results are expected to provide insights on how well each method deals with these factors in the diversification process. Experimental results suggest that some methods perform better in queries whose originally retrieved documents are more relevant to the query while other methods perform better when the documents are more diversified. We therefore propose methods to combine these existing methods based on the predicted factor of the query. The experimental results show that the combined methods can outperform individual methods on TREC collections.