Based on my research a recommendation system are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. And I'm currently developing a collaborative filtering recommendation system, and basically is recommends the top 'n' items to a user (I've used the user-item algorithm).
So on top of that, I will try to evaluate my recommendation system using the movie lens dataset, and basically from my research typical evaluation measures for top-N recommendation are normalized discounted cumulative gain (NDCG) and precision/recall. So my question is how can I evaluate using these metrics (or if you have any other suggestion for another metric) using the movie lens dataset (since it's a rating of users to items).
Thanks. Any suggestions are welcome.