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

Alessio Malizia, Kai A. Olsen, Tommaso Turchi, Pierluigi Crescenzi

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
52 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)608-623
Number of pages16
JournalInformation Processing and Management
Volume53
Issue number3
Early online date30 Jan 2017
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • Ant Colony Optimization
  • Cooperative systems
  • Evolutionary computation
  • Information filtering
  • Information retrieval
  • Recommender systems
  • World wide web

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