Best approach for A/B testing two different recommendation systems I have two recommendation systems for musical preference which make a list of predictions for a particular user based on the songs they have saved in their library. The user then rates how good each recommendation was out of 6. I will be evaluating the performance of the recommendation systems based on the average rating given to songs recommended by system A and system B.
Let's use A to denote a song recommended by system A, and B to denote a song recommended by system B. For a particular user, should the recommendations be (AAAAAA or BBBBBB)  or should they be (ABABABAB)? I have implemented the first for now, being (AAAAAA or BBBBBB). Thus, in the current system, each respondent will be randomly assigned A or be, and only get the recommendations from that system. Is this the right approach, or does only recommending 1 system to each respondent bias them against what the other system could have recommended ?
Let's assume that B is far better than A. If we only recommend the same system to each user, and a user listens to songs which are all system A, they would never had heard system B, and the ratings of A would probably have been different (lower) if they had listened to the better system too. Is the ABABAB approach the best one ? Which is the best method to evaluate the performance of each system while reducing bias ?
Thanks
If user rates how good each recommendation then ABABAB approach is the best one. There could be case when system A becomes better than B overtime, then having 2 system does not make sense.