Recommender Systems and Machine Learning
A set of different concepts and terms arose in the last few years: Big Data, Artificial Intelligence, Recommender Systems, Data Mining, Data Science, Machine Learning, Deep Learning and Neuronal Networks – just to mention a few of them. What is the difference and what do they have in common? This tutorial gives an overview of the major techniques and focusses on one particular method for personalization: Recommender Systems.
Currently, most users are not able to consume all offered items at once – nor in a life time. So they have to find a considered selection of those items they want to buy or consume. A recommender system helps its users to decide for products or media they might be interested in. Therefore, a set of different approaches can be utilized to get predictions of the users’ behaviors and preferences.
We will present different application areas for artificial intelligence in the modern connected world – with a special focus on these applications that make the users’ decisions more convenient, efficient and effective. Some basic concepts will be introduced and explained with simple and comprehensible examples of algorithms. Moreover, the tutorial addresses some common challenges as well as typical issues and discusses how service providers might overcome them.
The tutorial is of interest for you if…
- … you want to learn why some recommender systems present the same product again, and again and again – even after you already purchased it!
- … you are wondering why it is sometimes better to hire 40 employees than to apply 40 additional algorithms to improve your predictions.
- … you want to get a general understanding of machine learning and recommender systems or if you want to develop some ideas how to personalize your web service.