Unlike traditional recommendation algorithms, which use explicit and implicit user ratings and apply some sort of collaborative filtering techniques, product recommendations in the food and beverage industry have to be sensitive to the context of the recommendation (location, time, day, etc.). In addition, recommendations have to be very accurate in terms of ranking, as customers are hardly interested in browsing through a web of products (unlike in movies and apparels) when they expect the algorithm to accurately identify patterns in their food habits and respond accordingly.
The problem is how to incorporate the contexts of tens of millions of customer base and then incorporate reinforcement learning to track the behavioral changes in customers preferences. Arun Veettil explains how to incorporate user contextual information into recommendation algorithms and apply reinforcement learning to track continuously changing user behavior.
Arun Veettil is the chief architect at Skellam AI, a company dedicated to helping businesses develop custom AI solutions. Arun is a tech veteran with over 17 years of industry experience. For the last seven years, Arun has been working at the intersection of machine learning and product development. Previously, he worked at Point Inside, Nordstrom Advanced Analytics, the Walt Disney Company, and IBM. His expertise includes developing machine learning algorithms to run against very large amounts of data and building large-scale distributed applications. Arun holds a master’s degree in computer science from the University of Washington and a bachelor’s degree in electronics engineering from the National Institute of Technology, Allahabad, India.
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