Title: A secured context-aware tourism recommender system using artificial bee colony and simulated annealing

 

Author: Arup Roy; Madjid Tavana; Soumya Banerjee; Debora Di Caprio

 

Addresses:
Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, India
Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA19141, USA; Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, D-33098 Paderborn, Germany
Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, India
Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada; Polo Tecnologico IISS G. Galile, Via Cadorna 14, 39100, Bolzano, Italy

 

Journal: Int. J. of Applied Management Science, 2016 Vol.8, No.2, pp.93 - 113

 

Abstract: Context-aware recommender systems have been developed to consider users' preferences in various contextual situations. While designing such systems, one immediate concern, is to preserve the integrity of the recommender and minimise the attack probability of biased users who may indirectly influence the outcome of the system. Several algorithms have been developed to identify malicious users in contextual environments. In this paper, we propose a reputation-controlled fish school (RCFS) algorithm to identify trustable users and utilise them in recommendations. In addition, we propose a recommendation algorithm that replicates the behaviour of social insects using a hybrid artificial bee colony (ABC) and simulated annealing (SA) technique. Finally, we demonstrate that the resulting feedback strategies can increase the effectiveness of the recommenders' decisions.

 

Keywords: artificial bee colony; ABC; contextual recommender systems; fish school algorithm; reputation ratings; simulated annealing; trusted user detection; security; context awareness; tourism recommender systems; recommendation systems; user preferences; feedback strategies.

 

DOI: http://dx.doi.org/10.1504/IJAMS.2016.077014

 

Available online 17 Jun 2016

 

 

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