Personalized Smart Grocery Delivery with IoT Technology Using Collaborative Filtering Algorithms

Personalized suggestions may enhance the use of online food shopping, an already popular and handy service. But data sparsity and scalability problems restrict the current recommendation systems rely on user-based collaborative filtering algorithms. Smart grocery delivery with personalized suggestions is proposed in this system using item-based collaborative filtering algorithms and Internet of Things (IoT) technologies. In the system, smart appliances like fridges and scales track food supplies and consumption, and then, according to the user's preferences, they automatically place purchases on internet platforms. It makes use of item-based collaborative filtering algorithms to examine other users' evaluations and purchase histories in order to provide the user with relevant and varied product recommendations. The method is tested on a real-world dataset and contrasted with collaborative filtering methods rely on user input. Techniques outperform the competition in terms of suggestion accuracy, variety, and coverage while simultaneously decreasing delivery time and costs. By implementing our strategy, not only can online grocery shopping be made more enjoyable for users, but it can also spur the growth of e-commerce and the IoT.