Recommendation Explanation
For example: "Movielens thinks you'll enjoy Fight Club, given that you highly rated Donnie Darko and Pulp Fiction"
Sometimes I have no Idea why I've gotten a certain recommendation

Thanks for the feedback! We would like to add that feature.
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Marie commented
Correct me if I'm wrong but I think movielens uses item-item collaborative fitering. Wizard and Warrior might be pure collaborative filtering vs a mixture with content based filtering (such as additionally using year, genre and tag information).
I'd really like to know what's hiding behind the two algorithms, even in simple terms.
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Anonymous commented
This is an interesting proposition. I am puzzled about how this request could be met. I see currently there are four recommenders: "the peasant," "the bard," "the warrior," and "the wizard." I would like to see better descriptions of those recommenders, e.g., what makes "the warrior" different to "the wizard" since both are based on ratings. For that reason I prefer to know how these recommenders see a movie, that is, I imagine movies are more that an id tag but also have qualitative or quantitative aspects such as genre, director, actors, tags, etc., which may or may not play a part in how predictions are made. I understand some AI could make its decisions based on abstract structures that are not as easy to follow as decision trees. Thus, I imagine in some cases it may not be a trivial matter to describe how a recommendation or prediction comes out.
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Leonardo commented
Yes, much like pandora does for songs. I'd like to see this feature very much!