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Recommender systems

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It is increasingly the case that every website or platform users visit wants to give them recommendations, for videos, songs, products, articles, movies and more. Over the last few decades, the commercial world has realised how effective the data derived from user behaviours can be in determining what you want before you even want it, and recommender systems are what make this possible.

In this talk I will showcase four major approaches to making recommendations, from simple early systems to more modern machine learning models, discussing the data needed to make them work, the process, and their pros and cons.

This talk is part of the Churchill CompSci Talks series.

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