Title: Augmenting transportation-related recommendations through data mining
Authors: Alexis Lazanas, Nikos Karacapilidis
Addresses: IMIS Lab, MEAD, University of Patras, 26504 Rio Patras, Greece. ' IMIS Lab, MEAD, University of Patras, 26504 Rio Patras, Greece
Abstract: This paper reports on the exploitation of data mining techniques during the formulation of purposeful association rules out of the transactions| database of a transportation management system. The rules| construction is performed through an elaborated version of the AprioriTid algorithm. The proposed algorithm is generic and capable to construct such rules by creating a large set of related items. The constructed rules can be used by the system|s recommender module, which is responsible for providing recommendations to the associated users. The recommendation process takes into account the constructed rules and techniques that derive from the area of Collaborative Filtering (CF).
Keywords: data mining; knowledge association rules; recommender systems; profile personalisation; transport management; collaborative filtering.
International Journal of Advanced Intelligence Paradigms, 2010 Vol.2 No.1, pp.78 - 89
Published online: 30 Nov 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article