More talks in the program:
10:30 - 11:30
Personalization improves customer experience and revenue. Though Matrix factorization based Recommender Systems are still popular, the scientific community have invented newer techniques to address more intricate challenges of diversity, evolving tastes of users and cold start, to name a few.
With the exponential increase in use and access of online shops, online music, video and image libraries, search engines and recommendation system are the best ways to find what you are looking for (and sometimes, what you are NOT looking for).
I will share the practical implementation issues we faced while developing a recommendation system. I will also discuss the recent advances made in the field of recommendation systems using deep learning to address diversity, evolving user tastes, etc.
Attendees: Beginners and Intermediate skilled in Recommender systems