| Summary: | The hawker industry is known for its diverse food options and affordable prices, but customers often struggle to select their preferred hawker stalls due to the abundance of choices. To address this challenge, this research proposes the integration of business intelligence techniques with a recommendation marketplace system using content-based filtering. This research aims to enhance the hawker dining experience by providing personalised recommendations based on customer preferences and leveraging data-driven insights. The objectives of this study are to collect and integrate data from various sources, including hawker stall information, customer preferences, and feedback. This research will employ content-based filtering techniques to analyse this data and develop a recommendation engine that suggests hawker stalls and food options aligned with users' preferences and past interactions. Through this integration, this project seeks to derive valuable insights from the collected data, such as identifying popular food categories, analysing customer behaviour, and understanding customer satisfaction levels. These insights will enable us to optimise the recommendation marketplace system, improve operational efficiency, and enhance the overall customer experience. This paper presents a mobile-based recommendation system that allows customers to explore personalised hawker stall recommendations based on their preferences. By integrating business intelligence with a recommendation marketplace system for hawker food, this research contributes to improving the decision-making process for customers, promoting hawker stall visibility and revenue, and creating a personalised and enjoyable dining experience. © 2023 IEEE.
|
|---|