Week 1 

Introduction [Lecture 1]

Introduction on the techniques that will be presented at the lectures, and on the structure/organization of the course.

Collaborative Filtering [Lecture 2]

 

Discussion on the project topics.

Project examples

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Week 2 

Content-based Recommendations [Lecture 3]

Knowledge-based Recommendations [Lecture 4]

Hybrid Recommendations [Lecture 5]

Context: Definition, Preferences, Recommendations [Lecture 7]

 

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Week 3 

Recommendations beyond the Ratings Matrix [Lecture 6]

Recommendations beyond the Ratings Matrix: Data Integration [Lecture 8]

 

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Week 4 

 

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Week 5 

Group Recommendations [Lecture 9]

Result Diversification [Lecture 10]

Fairness in Group Recommendations [Lecture 11]

 

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Week 6 

Diversity in Recommender Systems [Lecture 12]

 

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Week 7 

 

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Week 8 

 

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GRADES
[Assignments]
[Projects]
[Final]