Food is a very personal choice. We at Swiggy are obsessed about Customer Experience and want to make food discovery on the platform seamless and a delight for the consumer. So when you fire the Swiggy app, We take your Implicit/explicit feedback to figure out Your Taste Preferences, Your Price Affinity, Single/Group Order, Breakfast/ Late night Cravings and provide a convenient, Simple but highly personalized food ordering experience.

Outline

We want to talk all about Art & Science of Food Discovery @Swiggy. How we use advanced Machine Learning/AI on terabytes of data ( implicit/Explicit Feedback ) everyday, to bring you recommendations that powers Restaurant Feeds, Filter Widgets, Personalized Collections.

We will also be talking about our Journey, Learning and Challenges of building Food Recommendation System.

• Overview
• Recommendation @Swiggy
• Evolution of Recommendation Systems
• CF & Content Based Methods
• Learning to rank
• Understanding Food Catalog
• Meals Recommendations.
• Page generation.

Requirements

Basic understanding of Machine Learning, NLP & Interest into Recommendation Systems.

Speaker bio

Nitin is a Senior Data Scientist @Swiggy. He is currently working on Relavence and Discovery. He has over 7 years experience working in companies like Swiggy, Groupon, Fidelity and [24]7-inc. He has worked on variety of business problems across different domain using Machine Learning, Text mining & NLP.

He holds Masters in Computational Linguistics from IIITH. He has 5 patents in the area of customer experience. He is also author of a book on NLP Tookkit “NLTK Essentials”.

https://www.linkedin.com/in/nitinhardeniya/
https://about.me/nitinhardeniya

Slides

https://www.slideshare.net/NitinHardeniya/our-experiments-with-food-recommendations-107732145