Ashay Tamhane
Staff Data Scientist
Swiggy
location_on India
Member since 4 years
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Ashay Tamhane
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Ashay has a Masters in Computer Science from SUNY Buffalo (2011), with specialisation 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.
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Food Recommendation at Swiggy
20 Mins
Talk
Intermediate
Do you like to explore new dishes every time you order food? Or do you stick to your usual favourites? 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.
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Modeling Contextual Changes In User Behaviour In Fashion e-commerce
20 Mins
Talk
Intermediate
Impulse purchases are quite frequent in fashion e-commerce; browse patterns indicate fluid context changes across diverse product types probably due to the lack of a well-defined need at the consumer’s end. Data from fashion e-commerce portal indicate that the final product a person ends-up purchasing is often very different from the initial product he/she started the session with. We refer to this characteristic as a ‘context change’. This feature of fashion e-commerce makes understanding and predicting user behaviour quite challenging. Our work attempts to model this characteristic so as to both detect and preempt context changes. Our approach employs a deep Gated Recurrent Unit (GRU) over clickstream data. We show that this model captures context changes better than other non-sequential baseline models.
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