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How to Evaluate a Recommendation System?
There is actually a broad set of approaches to exploit the feedback dator as well as to make use of additional data, which may be linked to users items. The most popular ones are actually colliborative filtering, but there are additional ones since all different methods come with their downsides and upsides. Most of the time we are talking about the relevance of recommendations shown by users that consume actually the item that we recommend to them. But it's not the only set of gold that are important in recommenda systems. It's also about surprise users, so serendipitous recommendations. It's about having a diverse set of recommendations about novel things really inspire people. Are people correctly represented?