Address Location Correction System for Q-commerce

Hyperlocal e-commerce companies in India deliver food and groceries in around 20-40 minutes, and more recently, some companies focus on sub-ten-minute delivery targets. Such "instant" delivery platforms referred to as quick (q)-commerce onboard GPS locations of customer addresses along with their text addresses to enable Delivery Partners (DPs) navigate to the customer locations seamlessly. Inaccurate GPS locations lead to a breach of promises on delivery times for customers and order cancellations because the DPs may not be able to find the address easily or may not even navigate close to the actual address. As a first step towards correcting these inaccurate locations, in this work, we design a classifier to identify if the GPS location captured is incorrect using the text addresses. The classifier is trained in a self-supervised manner. We propose two strategies to generate the train set, one based on location perturbation using Gaussian noise and another based on swapping pairs of addresses in a dataset generated with accurate address locations. An ensemble of outputs of models trained on these two datasets give 84.5 % precision and 49 % recall in a large Indian city on our internal test set.