Uncovering Insights for New Car Recommendations with Sequence Pattern Mining on Mobile Applications

Abstract

This study employs sequential pattern mining to analyze browsing behaviors and aid mobile app service providers in effectively promoting and recommending new products. We collected browsing history data from 66,004 mobile app users for new car info in Taiwan, totaling 1,263,614 records over two months. By utilizing sequence pattern mining, we identified frequent browsing sequences on the app that can indicate subsequence product interests and suggest new items to potential customers. The proposed method can improve the user experience for mobile app users and facilitate the development of the potential market for advertising. The study highlights the effectiveness of sequence pattern mining in recommending new products to car app users, benefiting small app vendors, improving user experience, and informing product development decisions in the automobile industry. Furthermore, the findings emphasize the importance of considering the sequential relationships between events or items in pattern mining, particularly in mobile app development. In conclusion, the proposed approach offers a cost-effective solution for small app vendors to recommend new products and improve the overall user experience, providing valuable insights for the automobile industry.