Fusion-based Methods for result diversification in web search

Shengli Wu, Chunlan Huang, Liang Li, Fabio Crestani

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
130 Downloads (Pure)


Search result diversification of text documents is especially necessarywhen a user issues a faceted or ambiguous query to the search engine.A variety of approaches have been proposed to deal with this issue in recent years.In this article, we propose a group of fusion-based result diversification methodswith the aim to improve performance that considers both relevance and diversity.They are linear combinations of scores that are obtained from different componentsearch systems. The weight of each search system isdetermined by considering three factors: performance, dissimilarity, and complementarity.There are two major contributions. Firstly, we find that all the three factors of performance and complementarity and dissimilarity are useful for effective weighting of linear combination.Secondly, we present the logarithmic function-based model for converting ranking information into scores.Experiments are carried out with four groups of results submitted to theTREC web diversity task. Experimental results show that some of the fusion methods that use the aforementioned techniques perform more effectively than the state-of-the-art fusion methods for result diversification.
Original languageEnglish
Pages (from-to)16-26
Number of pages11
JournalInformation Fusion
Issue number1
Early online date6 Jan 2018
Publication statusPublished (in print/issue) - 31 Jan 2019


  • Data fusion
  • Web search
  • Result diversification
  • Linear combination
  • Weight assignment
  • Linear score normalization


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