Recommendation systems are everywhere. For example, when our social network invites us to connect with other users, or when our subscription music service suggests new content. All these systems share some basic principles that we will review in this talk.
In this talk we will analyze the basic aspects of the functioning of the recommendation systems. The talk is divided into 5 parts:
We will begin by briefly discussing the origins and business opportunities that have generated the expansion of recommendation systems. We will analyze the main advantages from the point of view of companies and users.
Recommendations based on users.
Improving similarity computation.
Improving the calculation of predictions
Prediction of Ratings vs Recommendation of Top N Items
Evaluation of Systems Based on Collaborative Filtering.
Metrics for prediction.