Büyük veri ile menü eniyilemesi

The use of optimal menu structuring for different customer profiles is essential because of usability, efficiency, and customer satisfaction. Especially in competitive industries such as banking, having optimal graphical user interface (GUI) is a must. Determining the optimal menu structure is generally accomplished through manual adjustment of the menu elements. However, such an approach is inherently flawed due to the overwhelming size of the optimization variables' search space. We propose a solution consisting of two phases: grouping users and finding optimal menus for groups. In first part, we used H(EC)2 S , novel Hybrid Evolutionary Clustering with Empty Clustering Solution. For second part we used Mixed Integer Programming (MIP) framework to calculate optimal menu. We evaluated the performance gains on a dataset of actual ATM usage logs. The results show that the proposed optimization approach provides significant reduction in the average transaction completion time and the overall click count.