The Psychology of Deep Learning in Decision Making

Tech Triveni speaker Nitendra Rajput

Nitendra Rajput

Executive Vice President & Head - Data Science

Info Edge India Limited

About Nitendra Rajput

Nitendra Rajput works as Executive Vice President and Head of Data Science at Info Edge. In this role, he defines and leads the data science work for various Info Edge businesses such as naukri, jeevansathi, 99acres. Prior to this, Nitendra was at IBM Research for 18 years, managing the mobile and AI agenda for IBM Research in India.

With over 19 years of experience, Nitendra is considered to be an expert in the area of Artificial Intelligence and Mobile Interactions, having authored over 80 publications at top international ACM and IEEE venues, owing to which he has been recognized as an ACM Distinguished Scientist, ACM Distinguished Speaker and a senior IEEE Member. He has delivered several tutorials and conducted workshops in AI and speech areas at top ACM venues globally (MobileHCI, IUI, CSCW, CHI). With over 50 patents to his name, Nitendra was an IBM Master Inventor and was also an IBM Academy of Technology member. In 2012, he coauthored a book titled "Speech in Mobile and Pervasive Environments" that was published by John Wiley & Sons.


In this talk, we will demonstrate that deep learning has a very strong connection to the psychology of decision making. Several behavioural economists and psychologists have researched on the hidden parameters through which we learn and make decisions. We will talk about few such important life decisions rather than simplistic decisions like choosing a movie or buying a product on eCommence site. Key life decisions such as choosing a job, a life-partner or a house need recommender systems that go beyond normal list of features that are generally considered in systems that recommend movies or an online shopping item. That is where the power of deep learning is truly required because in normal life, humans make such key life decisions on several hidden parameters that are often not easy to extract, represent and model. In this talk, we will discuss about extracting and representing such hidden and latent features for two specific cases (a) job-CV matching, and, (b) life-partner matching.

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