This special issue of the Information Retrieval journal is focused on the topic of query intents and diversification within search engines. Indeed, information retrieval users have diverse search needs, and the level of detail given in the user’s search query can differ widely depending on how clear the underlying information need itself is, the user’s search environment (e.g. input device), and how difficult it is for the user to express the need in the form of a query. For these reasons, many queries are ambiguous and/or underspecified.

In light of this, research in accommodating different search intents has received a lot of attention lately. For example, major conferences like SIGIR, WWW and WSDM have begun to see papers on search result diversification, which aims to capture different user needs within one entry-point search result page. This problem was discussed intensively at the ECIR 2011 and WSDM 2012 Diversity in Document Retrieval Workshops. Also, in recent years, the TREC Web (2009–2012) and TREC Blog (2010) tracks have measured the diversity of participating systems in retrieving web pages and blog posts, respectively. Moreover, the recent INTENT tasks at NTCIR-9 and NTCIR-10 tackled not only search result diversification but also the task of mining intents given a query.

The aim of this special issue is to highlight the advances and clarify the future goals in the ares of search intents and diversification. In particular, the articles included in this special issue address two areas within this vision, namely the identification of query suggestions or sub-topics for the purposes of diversification based on those sub-topics, as well as the evaluation of diverse search results.

For example, in the article by Santos, Macdonald and Ounis, query suggestions were mined from a query log, and ranked using learning-to-rank techniques that are commonly applied to document retrieval. In the article by Wang, Lin, Tsai and Chen, sub-topics are mined from a variety of resources, namely Wikipedia, Open Directory Project as well as query logs. Finally, Wang, Qian, Song, Dou, Zhang, Sakai and Zheng propose an approach for mining sub-topic descriptions from the context surrounding the query terms within the document corpus.

With respect to the evaluation of diversified search results, Sakai and Song discuss the choice of evaluation metrics, the use of intent probabilities and per-intent graded relevance, and the topic set size using diversity test collections from both NTCIR and TREC. On the other hand, Golbus, Aslam and Clarke propose a family of diversity evaluation metrics and a meta-evaluation metric by considering how inherently difficult it is to diversify given a target corpus.