Short-Term course on "Recommender System" 

November 13 - 17, 2017    
Organized by E-Business Centre of Excellence    
Department of Industrial & Systems Engineering, IIT Kharagpur    

Registration Latest by 31st October, 2017













Recommender System helps business to increase their sales through cross-selling and advertisements. It discovers information about the items that are likely to be of interest to the users. More than 35% of product sales come from recommendations in, 66% of movies rented in Netflix are recommended ones, 38% more click-throughs are generated from recommendations in Google News. All these statistics tell about the importance of recommender system in the present day both from the perspective of end-user and industries running an online business. There are many challenges in designing the most suitable recommender systems in the context of specific real world scenarios. It opens the door of broad research opportunities in this application area of machine learning. Proposed course is designed to benefit both industry and academic participants by introducing the theoretical foundations and providing hands on practices for design and implementation of Recommender Systems.

Important Information  

Objectives of the Course

To build competency of the participants in broad area of recommender system and its applications.

Course Content

  • Content-based Recommender System
  • Collaborative filtering based Recommender System
  • Trust or Social Network based Recommender System
  • Hands-on Project using Python

Course Schedule

  • 9 am to 6 pm with 2-hour lunch break on each day.

Training Methods

  • The training methods consist of lecture sessions, hands-on-exercises, discussion on cases and live problems.


  • Category-I: Students
  • Category-II: Faculties
  • Category-III: Participants from the Industry working in related fields

Course Coordinator

Prof. Mamata Jenamani
Course Coordinator,
Associate Professor,
Department of Industrial & Systems Engineering,
IIT Kharagpur
Phone (Office): +91 - 3222 - 283740