Recommendation System for Surplus Food Management using Location based and Collaborative Filtering Approach
Nilesh Madhukar Patil, Ananya Doshi, Vansh Dodiya, Jenil Savla, Meera Narvekar · 2024
In various industries, recommendation systems are playing an increasingly important role in delivering improved services and customer experiences. In this study, a thorough overview of the suggested system and explanation of the techniques used is offered. Two recommendation systems are built into the Food Surplus App, the first one is a customised recommendation system built on the collaborative filtering mechanism. The Single Value Decomposition Algorithm is used to generate suggestions after computing the cosine similarity to get the similarity measure. The top 5 results and the user's prior history are used to present the results. The second system employs the K-means clustering technique to develop a location-based recommendation system. The clusters map the food item IDs of the restaurants to that location's availability.