I have a dataset of <user, item> pairs where each entry records which user bought which item.e.g.<u1, i1><u1, i4><u2, i2><u3, i2>...I created an encoded dataset withno_of_features = no_of_users + no_of_itemsand have set output variable y to 1, as each of those entries represented the user having bought that particular item.Note: All y values are 1 in this case.The encoded dataset looks like this:user1 user2 user3 .... item1 item2 item3 item4 .... y  1     0    0    ....   1     0     0     0   .... 1  1     0    0    ....   0     0     0     1   .... 1  0     1    0    ....   0     1     0     0   .... 1  0     0    1    ....   0     1     0     0   .... 1Now, I would like to know how to use fastFM to generate recommendations for cases <x, y> for any user x and any item y?Clearly regression is out of the picture here. So should I use the classification or ranking approach of fastFM? Also how? e.g. If I use classification, do I need to generate instances with y=0? If I use ranking approach, do I arrange items by the output of ranking approach and recommend those with higher values?
How to use Python's FastFM library (factorization machines) for recommendation tasks?
python;recommender system;scikit learn