Announcements | Centre for Modeling and Simulation | Savitribai Phule Pune University

Case-Study Session | Recommendation Systems: A Design Perspective

Title	Recommendation Systems: A Design Perspective
Speaker	Abhijit Kulkarni, SAS R&D Dr. Abhijit Kulkarni holds a PhD in Process Engineering from the University of Pune + National Chemical Laboratory, Pune. Currently, he is a Senior Analytical Consultant at SAS R&D, Pune, working in the area of statistical modeling, data mining and machine learning.
Date & Time	Saturday, 04 February 2017 \| 10:00-12:30
Venue	Kelkar Laboratory Centre for Modeling and Simulation, Savitribai Phule Pune University
Abstract	Recommendation systems have attracted a lot of attention recently. Lot of researchers are actively investigating several problems in this domain across several business verticals. There are numerous approaches proposed in the literature that can be mainly categorized as collaborative filtering based methods (originally proposed during Netflix Movie Recommendation challenge (http://www.netflixprize.com/)), content based methods, hybrid methods (combining collaborative filtering and content based methods), and recently proposed knowledge based methods. Many algorithms are proposed in each of these categories, mainly comprising algorithms using matrix factorization methods, algorithms using similarity metrics, machine learning based approaches, graph theoretic methods etc. There are several challenges one can face while designing and evaluating a recommendation system on real world problems. In this session, I will cover the following topics: Overview of recommendation systems Challenges from the data perspective Few open problems Case Study using real data
Organizer/Host	VK Jayaraman and Mihir Arjunwadkar, Centre for Modeling and Simulation, Savitribai Phule Pune University

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