Input: Observed feedback and auxiliary feedback
Output: Parameters
Initialization: for ;; do
   Split n items into three parts: , , ;
end
for iterations do
for training sample do
   Step 1. Uniformly sample a user ;
   Step 2. Uniformly sample an item i from ;
   Step 3. Uniformly sample an item k from ;
   Step 4. Uniformly sample an item j from ;
   Step 5. Calculate ;
   Step 6. Update via (17), (24);
   Step 7. Update via (18), (25) and the latest ;
   Step 8. Update via (19), (26) and the latest ;
   Step 9. Update via (20), (27) and the latest ;
   Step10. Update via (21), (28);
   Step11. Update via (22), (29);
   Step12. Update via (23), (30);
  end
end
Algorithm 1: The algorithm of Medical Bayesian Personalized Ranking over multiple users’ actions.