An App-Based Recommender System Based on Contrasting Automobiles

Product recommendation systems are essential for enhancing customer experience, and integrating them with mobile apps is crucial improving usability fostering user engagement. This study proposes a hybrid approach that utilizes comparative facts from pairwise comparison data lists, association rules as the method to formulate system. The employs dataset New-Cars Database app, comprising 30,867 vehicle comparisons made by 5327 users across 40 car brands 870 cars 30 January 2015 2 April 2015. Two metrics developed measure system’s output under varying support confidence thresholds. findings suggest adjusting values can improve breadth depth of product recommendations. In addition, unit analysis affect output, lists supplementing expanding exploration potential outcomes. proposed aims provide more reliable comprehensive recommendations combining both approaches has implications academic managerial contexts facilitating development effective systems.