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AI240: Introduction to Recommendation Systems

Training: Artificial Intelligence

Participants receive a practical introduction to recommender systems. The course covers fundamentals, scoring, and system types such as collaborative filtering, content-based, hybrid, and semantic recommender systems. It also addresses data sources, data collection, processing, and evaluation metrics to successfully develop custom systems.

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Possible course languages: German

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Would you like to request an appointment? Then click on 'No matching appointment?'

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Agenda:

Introduction to recommender systems and their significance

Comparison of different recommender systems: Collaborative filtering, content-based filtering, hybrid recommender systems, semantic recommender systems

Data sources, data acquisition and processing: Insight into common data sources and techniques for data acquisition, data preprocessing and analysis

Evaluation metrics for recommender systems: Presentation of various metrics for assessing the quality of recommendations

Practical exercises: Development of a simple recommender system with Python

Summary and final discussion: How can the learned knowledge be transferred to one's own context?

Objectives:

Understand fundamentals of recommender systems, learn different types of recommender systems, master data sources and processing for recommender systems, apply evaluation metrics for recommender systems.

Target audience:

Developers
IT professionals
Professionals without programming experience

Prerequisites:

AI200 Introduction to Python for Data Science and AI (alternatively basic knowledge in Python)

Description:

From the music you listen to, to the products recommended to you online: Our workshop AI240 Introduction to Recommendation Systems will demystify the world of recommendation algorithms and provide you with the tools to build your own high-performance recommendation system.

The course teaches fundamentals of building, functionality and use cases of such recommendation systems. Participants will be enabled to understand what benefits such a system has, what application scenarios exist and how to deploy it themselves. For this purpose, the training covers concepts such as scoring and building a recommendation system, but also different types of these systems (Collaborative Filtering, Content-based Filtering, Hybrid Recommender Systems and Semantic Recommender Systems). Additionally, data sources, data collection and processing as well as evaluation metrics for recommender systems are addressed.