title: Evaluating Content-Based Recommender System (where there no "users")
body: <p>I am building a content-based RecSys for football players, using the FIFA 2020 dataset. In this case, the players are the "items", and their scores (heading ability, dribbling skills, positional scores etc) are the "ratings". I have already created the cosine similarity matrix to rank each player's similarity scores. My model simply aims to do this: for any given player, my model will display the top 5 most similar other players.</p>
  
  <p>I am now at the stage of evaluating my model, and am facing some issues. Having done some readings online, I gathered that I probably CANNOT use statistical measures like RMSE or MAE. The more appropriate one would be Precision and Recall (or more specifically, Mean Average Precision MAP).</p>
  
  <p>The thing is, all these evaluation metrics require an actual user to validate it (e.g. to actually CHOOSE Cristiano Ronaldo as a "correct" similar player to Lionel Messi). And going back to the original dataset, it's not like a case of movie ratings/Amazon items where the item was actually rated by different users - FIFA 2020 is simply a collection of each player's scores and attributes. Hence, I am unable to "predict" another user's ratings for another player and do recommendations based on that.</p>
  
  <p>Hence, I would like to ask if there any other suggestions to evaluate my model, in terms how "correct" the recommended similar players are? Once again, <strong>my model simply aims to display the top 5 most similar players for any given player.</strong></p>