Multi-dimensional search result diversification

  • Authors:

  • Zhicheng Dou;Sha Hu;Kun Chen;Ruihua Song;Ji-Rong Wen

  • Affiliations:

  • Microsoft Research Asia, Beijing, China;Renmin University of China, Beijing, China;Xi'an Jiaotong University, Xi'an, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the fourth ACM international conference on Web search and data mining
  • Year:
  • 2011

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Abstract

Most existing search result diversification algorithms diversify search results in terms of a specific dimension. In this paper, we argue that search results should be diversified in a multi-dimensional way, as queries are usually ambiguous at different levels and dimensions. We first explore mining subtopics from four types of data sources, including anchor texts, query logs, search result clusters, and web sites. Then we propose a general framework that explicitly diversifies search results based on multiple dimensions of subtopics. It balances the relevance of documents with respect to the query and the novelty of documents by measuring the coverage of subtopics. Experimental results on the TREC 2009 Web track dataset indicate that combining multiple types of subtopics do help better understand user intents. By incorporating multiple types of subtopics, our models improve the diversity of search results over the sole use of one of them, and outperform two state-of-the-art models.