Learner course recommendation in e-learning based on swarm intelligence

Se dan unas recomendaciones en la enseñanza asistida por ordenador (e-learning) basada en la inteligencia colectiva.This paper analyses aspects about the recommendation process in distributedinformation systems. It extracts similarities and differences between recommendations in estores and the recommendations applied to an e-learning environment. It also explains the phenomena of self-organization and cooperative emergence in complex systems coupled with bio-inspired algorithms to improve knowledge discovery and association rules. Finally, the present recommendation is applied to e-learning by proposing recommendation by emergence in a multi.agent system architecture

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

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.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.Peer reviewedFinal Accepted Versio