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A recommendation system (RS) is used to provide recommendations to users by filtering items based on given inputs. Metaheuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Optimisation (ACO) are known to be used in many RS to provide optimal and good recommendations. Both algorithms are designed based on nature-inspired events, where GA is designed based on the natural evolution process while ACO is based on ants’ behaviour in their natural habit. In this paper, both GA and ACO algorithms were implemented in a restaurant RS and evaluated by using the restaurant’s attributes, which was then followed by a list of recommended restaurants as the output. With the highest score of 99.64% of accuracy, GA overtakes ACO in terms of recommendation accuracy while ACO computed 67.12% lesser runtime than GA. Considering the results acquired, a new hybrid framework known as the HGA-ACO algorithm was proposed. The proposed HGA-ACO has a recommendation accuracy of 99.57% and achieved a 31.37% runtime reduction from GA. Thus, the proposed framework was observed to have improved the output accuracy of ACO and improved the processing time in GA, thus, improving the overall efficiency of the RS. |
