NICF- Recommender Systems (SF)

NUS Institute of Systems Science

How long?

  • 3 days
  • in person

What are the topics?

NUS Institute of Systems Science

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Who should attend

This course is intended for data analysts and marketers who are interested in using data and analytics to understand customer behaviours and preferences and to make personalised recommendations to users

Pre-requisites

This is an intensive, advanced course. Participants with some exposure to working with data using tools like R will benefit more from the course. Participants with limited knowledge may consider acquiring them via NICF-Statistics Bootcamp course.

About the 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 Analytics and Intelligent Systems series & Stackable Certificate Programme in Business Analytics offered by NUS-ISS.

Key Takeaways

At the end of the course, the participants will be able to:

  • Understand the role and applications of recommendation systems
  • Identify the types of data necessary for building a recommendation system
  • Understand the main types of recommender system and be able to decide when each should be used
  • Build recommendation systems using statistical modelling
  • Enhance recommendation systems based on testing and valid

This course will cover :

  • Introduction to Recommender Systems
  • Making recommendations using Market Basket Analysis methods
  • Making recommendations using Content-Based approaches
  • Making recommendations using Collaborative Filtering (part A)
  • Making recommendations using Collaborative Filtering (part B)
  • Advanced Recommender Systems Approaches & Issues

Experts

Barry Adrian Shepherd

Barry teaches Business Analytics, Data Mining and Knowledge Engineering at ISS and has over 30 years experience in these areas. Before joining ISS he was based in the US and specialized in web analytics and user response modeling for online ad targeting at Microsoft Display Advertising and in pro...

NICF- Recommender Systems (SF) at NUS Institute of Systems Science

From  2889 SGD$2,128

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