Recommender Systems 2017

Course Description

We will get familiar with the concept of Recommender Systems and cover some of state-of-the-art algorithms in this field.
By the end of this course, you will be able to handle new data and to supply meaningful recommendations using a variety of algorithms on real-life data sets.

Recommender Systems is a fast growing field, and gained a lot of attention in recent years. These Systems aim at suggesting relevant and valuable items to users. In the core of the system resides the engine that predicts per each user the personalized importance of each item.

In this course, we will learn advanced algorithms based on Machine Learning, while focusing on Collaborative Filtering techniques. These techniques infer the preferences of a given user from decisions made by other users, where the most common such approach is Matrix Factorization.

Course topics

Collaborative Filtering, Matrix Factorization

Course tools



Machine Learning


Oren Sar Shalom
A research staff member at IBM Research and a Ph.D candidate in Computer Science in the field of Recommender Systems at Bar Ilan University, Israel

Holds a B.Sc, M.Sc in computer science, an MBA and finalizing a Ph.D in computer science. With more than 10 years of experience in the industry, Oren is a research staff member at IBM Research, where he conducts research is on NLP related problems using Deep Learning. He is also the lecturer of the Recommender Systems course in the University of Haifa and a co-chair of the workshop on Deep Learning for Recommender Systems (DLRS 2017).

Fields of interests: Machine Learning, Recommender Systems, NLP.