Mobile Application for Personalized Food Recommendation
Aryan Jalali, Kumar P. Arjun Manoj, Nallavalli Amulya, Ayesha Siddiqua, Alok Kumar Singh, S. M. Hari Krishna · 2022 IEEE 7th International conference for Convergence in Technology (I2CT) · 2022
Personalization in business products has always proven to be a powerful marketing approach by which businesses collect data from users, analyze data and use Machine Learning (ML) algorithms to deliver relevant personalized content to the user. ML has shown to be a ground breaking technology that can be used in the everyday decision-making processes of our lives. A personalized food recommender caters to the exact need and taste of the user taking in account factors like the weather, temperature, type of diet and time of the day. This paper illustrates the design of the application that takes inputs from the user, analyzes the data and feeds it to the machine learning algorithm. The algorithm works on the concept of content-based filtering and a collaborative filtering model. An item feature similarity and item-user similarity matrices are created. Based on these values most similar food items are recommended on the application.