Recommendation System Training - Hong Kong

Recommendation System Training​ Course Overview

A Recommendation system is an extensive class of web applications comprising predicting the user responses to the options. It is a data filtering tool that analyses historical data for predicting what users will be interested in and create accurate recommendations. This system is mostly used in social media, e-commerce platforms, and content-based services. This Recommendation System Training is designed to equip delegates with a knowledge of all the fundamental techniques in the recommender system.

In this Recommendation System Training, delegates will learn about basic concepts of recommendation systems. Delegates will get an understanding of model-based and preprocessing-based approaches. In addition, delegates will learn how to interact with constraint and case-based recommenders.

During this 1-day training, delegates will gain extensive knowledge of hybrid recommendation approaches. This course will introduce delegates to explanations in constraint, case, and collaborative based recommenders. Post completion of this course, delegates will be able to evaluate recommender systems.

Prerequisites

There are no prerequisites to attend this course.

Audience

Anyone wishes to have a comprehensive knowledge of Recommendation system can attend this course. This course is well-suited for:

• Machine Learning Engineers
• Data engineers and Scientists
• Data Analysts

Recommendation System Training Course Outline

Introduction to Recommender Systems

• What is Recommender System?
• Goals of Recommender System
• Basic Models of Recommender System
• Domain-Specific Challenges

Collaborative Recommendation

• User and Item Based Neighbor Recommendation
• Model-Based and Preprocessing-Based Approaches
• Practical Approaches and Systems

Knowledge-Based Recommendation

• Knowledge Representation and Reasoning
• Interacting with Constraint-Based Recommenders
• Interacting with Case-Based Recommenders

Hybrid Recommendation Approaches

• Opportunities for Hybridisation
• Monolithic Hybridisation Design
• Parallelised Hybridisation Design
• Pipelined Hybridisation Design

Recommender Systems Explanations

• Constraint-Based Recommenders
• Case-Based Recommenders
• Collaborative Filtering Recommenders

Evaluating Recommender Systems

• Properties of Evaluation Research
• Popular Evaluation Designs
• Evaluation of Historical Datasets
• Alternate Evaluation Designs