I am a Computer Scientist at Adobe Research Labs India, Bangalore. I joined Adobe in April 2014 and am currently looking at challenges in improving personalization in digital marketing. My research interests include Machine Learning, Complex Networks, Information Retrieval, Text Mining, Natural Language Processing, and Linguistics. Details of my research publications can be found here.
I completed my PhD as a Microsoft Research India PhD Fellow in March 2014 from the Department of Computer Science and Engineering, IIT Kharagpur. My PhD research was on the syntactic analysis of Web search queries, and I was jointly advised by Prof. Niloy Ganguly (IIT Kharagpur) and Dr. Monojit Choudhury (Microsoft Research India). I was a part of the Complex Networks Research Group (CNeRG) at IIT Kharagpur. Prior to my PhD, I obtained a B.E. in Information Technology from Jadavpur University, Kolkata, in 2007, and an M.Tech. in Information Technology from IIT Roorkee in 2009.
Moumita Sinha, Rishiraj Saha Roy(Oct 6, 2014)
Identification of relevant product attributes is critical to the success of any marketing campaign. This task can be conceptualized as an attribute recommendation problem based on the product’s content or features, where the goal of a solution would be to automatically recommend relevant features to the marketer for highlighting in a campaign. In this research, we try to solve this problem by using preference mapping, a powerful technique for associating feature preferences with users. We perform preference mapping with sentiment scores associated with product attributes mined from user reviews on theWeb. As a result of this process, we are able to visualize a set of compared products and the appropriateness of the attributes on the same two-dimensional space, enabling us to easily recommend important features to a marketer. Finally, we show that expert recommendations or ratings for product features do not necessarily correlate with preference maps based on user sentiments.
Workshop on New Trends in Content-based Recommender Systems 2014 (CBRecSys '14)