# Uncovering Insights for New Car Recommendations with Sequence Pattern Mining on Mobile Applications
## 1 Introduction  
Mobile applications have become an essential platform for businesses to communicate with users, gather valuable data on user behavior, and recommend new products. The user interface of mobile apps is a crucial aspect that can significantly impact user engagement and satisfaction [1]. An effective and well-designed product recommendation function within a mobile app can enhance the customer experience, increase customer loyalty, and provide valuable insights for new product development, ultimately leading to increased revenue for the business.  
However, traditional product recommendation techniques, such as association rules or k-Nearest Neighbors [2], have limitations in capturing the sequence of events or objects, leading to significant patterns being overlooked [3, 4]. This limitation is particularly significant in the case of new or technological products, as individuals are increasingly interested in the latest models and features. Sequential pattern mining has been suggested as a promising alternative to address this limitation. However, while several studies have improved the efficiency at which sequence rules can be generated [5, 6, 7, 8, 9, 10, 11, 12], few researchers have made their software code public [4], and only a few have been carried out on mobile app data, posing a challenge for small app vendors with limited financial resources who depend on ads to provide free services to their users.  
Therefore, this study proposes a cost-effective approach to new products and advertisement recommendations for small app vendors using a car info app as a case study. The study aims to address three research questions:  
1. How can sequential pattern mining effectively recommend new products to users of a car info app, especially for small app vendors with limited financial resources?What are the browsing behaviors of app users interested in car information, and how can these behaviors be analyzed to provide more intelligent and precise marketing recommendations?
3. How can automobile companies use insights from analyzing user behavior and preferences to make sound decisions about creating new products?  
This study contributes to browsing data analysis and mobile app marketing by providing insights into sequential pattern mining for improving product recommendations and understanding user behavior in the context of a car information app. The study's findings can help small app vendors provide effective recommendations for new products or advertisements and enhance their marketing strategies. Moreover, the insights can benefit automobile companies in tracking consumer trends about car model preferences to make sound decisions regarding creating new products.  
The following sections include a literature review on sequential pattern mining and a description of the data collection and methodology. The study also explains the metrics used to evaluate the discovered sequence rules. Next, the results and discussion sections present the study's findings, including insights, implications for new product recommendations, limitations, and future research. Finally, the conclusion section summarizes the study's results.