Recommender Systems
About This Course
Everyday decisions, from which products to buy, movies to watch and restaurants to try, are more and more being put in the hands of a new source: recommendation systems. Recommendation systems work by studying the past behaviours and purchases of users along with their preferences and product ratings. Using these and other relevant data they are able to provide recommendations and choices of interest to users in terms of �Relevant Job postings�, �Movies of Interest�, �Suggested Videos�, �People who bought this also bought this� etc.
Recommender systems have been widely used by online shopping companies such as Amazon.com. They play a critical role in analysing customer transactions and web browsing behaviours to provide sound recommendations for their customers, contributing to sales revenue and profitability. In this regard, a reliable and efficient recommendation system is essential for many companies� market and business success.
This course is part of the Data Science and Graduate Certificate in Business Analytics Practice Series offered by NUS-ISS.
What You'll Learn
1. Introduction to Recommender Systems
2. Making recommendations using Market Basket Analysis methods
3. Making recommendations using Content-Based approaches
4. Making recommendations using Collaborative Filtering (part A)
5. Making recommendations using Collaborative Filtering (part B)
6. Advanced Recommender Systems Approaches & Issues
Entry Requirements
Please see course weblink for more information