Understanding how good a collaborative filtering system is can be broadly determined by measuring three types of accuracy parameters, namely:
These measures help to understand how accurately the recommender works. These measures work by calculating the differences between previously rated items and their ratings estimated by the recommender system.
Decision Support Metrics (a.k.a Confusion Matrix)
These measures are used to find how well a supervised learning algorithm has performed.
Ranking Accuracy Metrics
These metrics are used to find out how well the recommender has placed the items in the final recommended list.
