DataHour: Self-Supervised Address Correction for Q-Commerce

DataHour: Self-Supervised Address Correction for Q-Commerce

07 Jun 202413:06pm - 07 Jun 202414:06pm

DataHour: Self-Supervised Address Correction for Q-Commerce

About the Event

Accurate customer locations for quick-commerce is a necessary requirement for under 30-minute online food and grocery delivery systems. Inaccurate locations can negatively impact delivery time promises, ranking of relevant restaurants and the accessibility of restaurants to customers. GPS locations captured using the customer smart-phone application can be inaccurate due to scattering of electromagnetic signals by vegetation or buildings or metallic reflectors as well as due to manual error in placing the GPS pin on the application.

In this talk, we discuss the design of a location correction system for q-commerce as a cascade of a location inaccuracy classifier and a geocoder. Apart from the address text and customer location inputs, we leverage signals collected from the delivery partner’s application. Using text and numeric inputs derived from these signals, we design a self-supervised multimodal architecture for the location inaccuracy classifier. We also touch upon the shortcomings of known geocoders and ways to make them production grade for q-commerce.

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Who is this DataHour for?

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About the Speaker

Abhinav Ganesan

Abhinav Ganesan

Senior Data/Applied Science Manager at Swiggy

Abhinav Ganesan is a Senior Data/Applied Science Manager at Swiggy. His current position involves designing and commercializing AI driven solutions for location intelligence and recommendation systems. His prior experience spans working on computer vision for ADAS at Netradyne Technology and signal processing algorithms for wireless modems at Qualcomm. He holds a PhD in Information Theory from the ECE department, Indian Institute of Science, Bangalore, and a post-doctorate in the same field from the Chinese University of Hong Kong.

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