Abstract: Do you like to explore new dishes every time you order food? Or do you stick to your usual favorites? Do you like a Raita with your Biryani or just a soft drink? Do you prefer veg or non-veg? Do these preferences change depending on time of day or day of the week?
These are among the several questions that need to be automatically inferred from data while building a food recommendation engine at scale. Right from ranking dishes on the menu to suggesting complimentary dishes on the cart, Data Science is at the very core of constantly improving our suggestions. In this talk, we will dive into multiple scenarios that pop up while building a food recommendation system in terms of handling data sparsity and handling new restaurants/users among other challenges that crop up due to scale. We will look at different algorithms that need to be explored in order to effectively handle these various challenges.
Bio: Ashay has a Masters in Computer Science from SUNY Buffalo (2011), with a specialization in AI. He is currently working as a Staff Data Scientist with Swiggy. Ashay has 9 years of experience building Machine Learning models in industries like education and e-commerce. He was heading the Customer Experience DS team at Myntra-Jabong, where they leveraged Machine Learning for enhancing customer experience. Before Swiggy, he has worked with Myntra-Jabong, IBM Research, and HP Labs.