The following algorithms will be discussed at length:
General non-personalized recommendations for a category of items
User-user collaborative filtering
User-user collaborative filtering with variations on similarity measures
Item-item collaborative filtering
Item-item collaborative filtering with variations
You will also learn how to evaluate recommendation engines using several evaluation metrics for different purposes, such as prediction accuracy, ranking accuracy in the context of top-N recommendations, and so on.