University of Hertfordshire

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

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An ant-colony based approach for real-time implicit collaborative information seeking. / Malizia, Alessio; Olsen, Kai A.; Turchi, Tommaso; Crescenzi, Pierluigi.

In: Information Processing and Management, Vol. 53, No. 3, 01.05.2017, p. 608-623.

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Malizia, Alessio ; Olsen, Kai A. ; Turchi, Tommaso ; Crescenzi, Pierluigi. / An ant-colony based approach for real-time implicit collaborative information seeking. In: Information Processing and Management. 2017 ; Vol. 53, No. 3. pp. 608-623.

Bibtex

@article{4de49d2ab7e9430d983abd0623dd06d1,
title = "An ant-colony based approach for real-time implicit collaborative information seeking",
abstract = "We propose an approach based on Swarm Intelligence — more specifically on Ant Colony Optimization (ACO) — to improve search engines{\textquoteright} performance and reduce information overload by exploiting collective users{\textquoteright} 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 — Na{\"i}veRank, RandomRank, and SessionRank — leverage on different principles of ACO in order to exploit users{\textquoteright} 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{\textquoteright} intent.",
keywords = "Ant Colony Optimization, Cooperative systems, Evolutionary computation, Information filtering, Information retrieval, Recommender systems, World wide web",
author = "Alessio Malizia and Olsen, {Kai A.} and Tommaso Turchi and Pierluigi Crescenzi",
note = "This document is an Accepted Manuscript of the following article: Alessio Malizia, Kai A. Olsen, Tommaso Turchi, and Pierluigi Crescenzi, {\textquoteleft}An ant-colony based approach for real-time implicit collaborative information seeking{\textquoteright}, 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: https://doi.org/10.1016/j.ipm.2016.12.005, published by Elsevier Ltd. ",
year = "2017",
month = may,
day = "1",
doi = "10.1016/j.ipm.2016.12.005",
language = "English",
volume = "53",
pages = "608--623",
journal = "Information Processing and Management",
issn = "0306-4573",
publisher = "Elsevier Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - An ant-colony based approach for real-time implicit collaborative information seeking

AU - Malizia, Alessio

AU - Olsen, Kai A.

AU - Turchi, Tommaso

AU - Crescenzi, Pierluigi

N1 - 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: https://doi.org/10.1016/j.ipm.2016.12.005, published by Elsevier Ltd.

PY - 2017/5/1

Y1 - 2017/5/1

N2 - 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.

AB - 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.

KW - Ant Colony Optimization

KW - Cooperative systems

KW - Evolutionary computation

KW - Information filtering

KW - Information retrieval

KW - Recommender systems

KW - World wide web

UR - http://www.scopus.com/inward/record.url?scp=85010842037&partnerID=8YFLogxK

U2 - 10.1016/j.ipm.2016.12.005

DO - 10.1016/j.ipm.2016.12.005

M3 - Article

AN - SCOPUS:85010842037

VL - 53

SP - 608

EP - 623

JO - Information Processing and Management

JF - Information Processing and Management

SN - 0306-4573

IS - 3

ER -