A secured context-aware tourism recommender system using artificial bee colony and simulated annealing. (2016)

• Record Type: Journal Article
• Authors: Roy, Arup; Tavana, Madjid; Banerjee, Soumya; Caprio, Debora Di
• Is Part Of: International journal of applied management science. Volume 8:Number 2(2016)
• Page Start: 93
• Page End: 113
• Publication Date: 2016
• Subjects: 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; Management science -- Periodicals; 658
• Languages: English
• ISSNs: 1755-8913

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.