University of Hertfordshire

An ant-colony based approach for real-time implicit collaborative information seeking

Research output: Contribution to journalArticlepeer-review



  • Alessio Malizia
  • Kai A. Olsen
  • Tommaso Turchi
  • Pierluigi Crescenzi
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Original languageEnglish
Pages (from-to)608-623
Number of pages16
JournalInformation Processing and Management
Early online date30 Jan 2017
Publication statusPublished - 1 May 2017


We propose an approach based on Swarm Intelligence — more specifically on Ant Colony Optimization (ACO) — to improve search engines’ performance and reduce information overload by exploiting collective users’ behavior. We designed and developed three different algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms — NaïveRank, RandomRank, and SessionRank — leverage on different principles of ACO in order to exploit users’ interactions and provide them with more relevant results. We designed an evaluation experiment employing two widely used standard datasets of query-click logs issued to two major Web search engines. The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users’ intent.


This document is an Accepted Manuscript of the following article: Alessio Malizia, Kai A. Olsen, Tommaso Turchi, and Pierluigi Crescenzi, ‘An ant-colony based approach for real-time implicit collaborative information seeking’, Information Processing & Management, Vol. 53 (3): 608-623, May 2017. Under embargo until 31 July 2018. The final, definitive version of this paper is available online at doi:, published by Elsevier Ltd.

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