Streamlining food takeaway at Food-courts using Collaborative Filtering and Analytics
Vedant Deshmukh, Apoorwa Phursule, Pranali K. Kosamkar, Dev Lunagariya, Ronit Gangshettiwar, Soham Agashe · 2024
The canteen management and quick-service restaurant ordering app represent a holistic solution aimed at optimizing food ordering processes and enriching user experiences. Through the utilization of collaborative filtering techniques, the app furnishes personalized food recommendations to users, drawing insights from both their individual preferences and the selections of similar users. Complementing this feature, the integration of D3.js empowers restaurant managers with comprehensive analytics and visualizations, facilitating data-driven decision-making concerning menu offerings, inventory management, and staffing schedules. User interfaces tailored for students and vendors ensure effortless navigation through menus, efficient cart management, and transparent order tracking. This research paper outlines the app’s design, implementation, and evaluation, spotlighting the integration of collaborative filtering and D3.js analytics as pivotal components enhancing the efficiency and efficacy of food ordering processes. Performance evaluation utilizing the Normalized Discounted Cumulative Gain (NDCG) metric revealed collaborative filtering’s superiority, yielding a notable score of 0.89 compared to popularity-based recommendations. Synthetically generated data facilitated rigorous testing, affirming the efficacy of the collaborative filtering approach. Anticipated benefits encompass a tailored and convenient ordering journey for customers, streamlined order management for vendors, and actionable insights poised to enhance operational efficiency and profitability. Ultimately, the app endeavors to bridge the gap between customers and vendors, fostering a seamless and gratifying food ordering experience within canteens and quick-service restaurants.