The Basic Recommender Systems course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of different recommender system alternatives.
After completing this course, you'll be able to describe the requirements and objectives of recommender systems based on different application domains. You'll know how to distinguish recommender systems according to their input data, their internal working mechanisms, and their goals. You’ll have the tools to measure the quality of a recommender system and incrementally improve it with the design of new algorithms. You'll learn as well how to design recommender systems tailored for new application domains, also considering surrounding social and ethical issues such as identity, privacy, and manipulation. Providing affordable, personalised and high-quality recommendations is always a challenge! The course also leverages two important EIT Overarching Learning Outcomes (OLOs), related to creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the predictions. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and strategies in different and innovative scenarios, for a better quality of life.
RT
There is a nice introduction to recommender systems field
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This course is really not worth the money. You can take it as a very shallow introduction to recommender systems but don't pay for it. There are a lot of free available materials online. The content is really poor. Apart from the introductory (videos) that are of poor quality by the way, the others lessons are not even given by a real person. They are just slides red by a machine. But the worst part is that apart from the quizzes there is 0 home assignments. The only assignments are things like: Take some time to think about this and write your thoughts in this textbox => USELESS
Wish there were notebook examples and/or more hands on.
I learned a lot from this quick course about Recommender systems.
There is a nice introduction to recommender systems field
This instructor is perhaps the best from among all courses I have learnt in Coursera. He taught the concepts very clearly, reminding the concepts from previous modules wherever necessary to compare or explain new concept in detail. The lecture slides present the concepts clearly. The question prompts in the videos are very useful, making us think what was learnt and apply it to find the answer to the prompt. Each module is split properly into small videos that are each focusing on a single topic, rather than trying to mix the introduction of multiple topics. Request Prof. This instructor is perhaps the best from among all courses I have learnt in Coursera. He taught the concepts very clearly, reminding the concepts from previous modules wherever necessary to compare or explain new concept in detail. The lecture slides present the concepts clearly. The question prompts in the videos are very useful, making us think what was learnt and apply it to find the answer to the prompt. Each module is split properly into small videos that are each focusing on a single topic, rather than trying to mix the introduction of multiple topics. I request Prof. Paolo to offer more courses in data sciences, artificial intelligence.
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