Once user-user filter was created , the final step would find ( for a specific user ) items that the user of interest, wasn't interacted before for each n similar users, then sort in a descendant way this items, by mean rating. Finally recommend the first m items.
similar_users=TopN_Plural(df,n,Usuarios) #list of lists
Recommender_list=[]
for i in range(0,len(similar_users):
for similar_user in similar_users[i]:
.
. #Finding the items fullfiling the previous characteristics, and storing them in a dictionary {ID_item:rating}
.
Recommend the top m items by ratings.
#Store this list in Recommender_list
return (Recommender_list)