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Ambiguous queries constitute a significant fraction of search instances and pose real challenges to web search engines. With current approaches the top results for these queries tend to be homogeneous, making it difficult for users interested in less popular aspects to find relevant documents. While existing research in search diversification offers several solutions for introducing variety into the results, the majority of such work is predicated, implicitly or otherwise, on the assumption that a single relevant document will fulfill a user's information need, making them inadequate for many informational queries. In this paper we present a search-diversification algorithm particularly suitable for informational queries by explicitly modeling that the user may need more than one page to satisfy their need. This modeling enables our algorithm to make a well-informed tradeoff between a user's desire for multiple relevant documents, probabilistic information about an average user's interest in the subtopics of a multifaceted query, and uncertainty in classifying documents into those subtopics. We evaluate the effectiveness of our algorithm against commercial search engine results and other modern ranking strategies, demonstrating notable improvement in multiple document scenarios.
Welch et al. (Mon,) studied this question.