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Our experiments with food recommendations @Swiggy
Submitted by nitin hardeniya (@noting) on Saturday, 31 March 2018
Section: Crisp talk Technical level: Intermediate
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.
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.
- Recommendation @Swiggy
- Evolution of Recommendation Systems
- CF & Content Based Methods
- Learning to rank
- Understanding Food Catalog
- Meals Recommendations.
- Page generation.
Basic understanding of Machine Learning, NLP & Interest into Recommendation Systems.
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 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”.