Refining a Novel AI Restaurant Recommender Application: A Systems Approach to Increasing User Engagement and Retention
Shreya Darbha, Katherine Fong, Kirsten Fung, Elizabeth Hunter, Seokhyun Chung, Robert J. Riggs · 2025
Deciding which restaurant to eat at often poses an inconvenience for many individuals. The decision-making process is riddled with a variety of factors such as personal preferences, social dynamics, and an overwhelming number of options. Our study addresses this issue by partnering with a startup, dinemait, that utilizes an artificial intelligence (AI) recommendation model to provide curated restaurant suggestions to its mobile application users. The goal of this work is centered around improving dinemait’s application to retain and grow its active user base. Our team used a systems-based approach to increase user engagement by: (1) evaluating the existing application, and (2) improving outreach features and techniques. After an internal review of the application, a study was conducted to gain user-centric data to further assess it. It consisted of focus group discussions and surveys to gain insight into necessary improvements and valuation of the application. Next, by researching specific marketing strategies and ideating push notifications to encourage interaction with the application, we provided suggestions for dinemait to deploy in order to gain exposure. Our results will provide us with insights into the viability of the current application and outreach tactics, to then guide our recommendations and implementation.