Automatic Evaluation of Recommendation Systems: Coverage, Novelty and Diversity
In a previous article, I wrote about the tree types of recommender systems:
However, it is extremely important to understand how can we evaluate recommendation systems automatically. This allows us to perform rapid development of the system we are working on, and evaluate different experiment parameters and choose the best performing one.
How to Evaluate Recommendation Systems
Earlier, many of the recommendation algorithms were evaluated based on their accurate predictions. However, this was not the best approach to evaluate recommender systems. Even though predicting users’ exact needs is crucial, it is not enough in most of the cases.
“A lot of times, people don’t know what they want until you show it to them” — Steve Jobs
That is why recommendation systems changed their perspective from being the most accurate engine to the best engine which allows people to…
