Lecture

Recommender Systems: Part 1

In course

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Description

This lecture covers recommender systems, focusing on collaborative filtering and content-based recommendation. It explains user-based and item-based collaborative filtering, similarity metrics, and aggregation functions. The instructor also discusses the cold-start problem, scalability, and matrix factorization for deriving latent factors.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_v199kc02

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Computer engineering

Software development: Human–computer interaction

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