<![CDATA[Abstract: Product recommendation systems play a crucial role in helping users discover productsthat align with their preferences, particularly on e-commerce platforms. However, the mainchallenge lies in improving recommendation accuracy to ensure that the suggested items are trulyrelevant. This study proposes the application of the Ant Colony Optimization (ACO) algorithmto enhance the accuracy of recommendation systems. ACO is a metaheuristic algorithm inspiredby the behavior of ants in finding the shortest path to a food source, which is adapted here tosearch for optimal product combinations based on users’ interaction history. Experimental resultsshow that integrating ACO with a collaborative filtering-based approach improvesrecommendation accuracy by up to 34% compared to conventional methods. These findingscontribute to the development of more intelligent and adaptive recommendation systems.]]>
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