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.