Interpreting negative scores from lightfm.LightFM.predict()

I saw some explanation from here regarding interpreting negative scores from the model.predict() method but I wanted to clarify a few points with the experts just for everyone to see as well.

I understand that the predicted scores don’t really mean anything but only used as a means to rank. Let’s say I have called lightfm.LightFM.predict() like this:

predictions = model.predict(
    user_ids=np.array([0, 0, 0, 0, 0]),
    item_ids=np.array([0, 1, 2, 5, 100]),
    item_features=feature_matrix
)

This means that I am predicting the score for user 0 on 5 items, namely 0, 1, 2, 5, 100. Let’s say that my result is:

array([ 2.79359961,  2.76859665, -6.60331917, -0.56102526,  1.27920794])

Does this mean that for user 0, we would predict item 3 (-6.60331917) to be the top recommendation , followed by item 4 (-0.56102526)… and item 1 (2.79359961) to be the lowest recommendation?

Thanks in advance!

[EthanRosenthal]

Almost. Everything you said is right, but the conclusion at the end is actually the reverse – the top recommendation is the item with the highest predict score, not the lowest. In your case, item 1 would be the top recommendation, and item 3 would be the lowest.

[EthanRosenthal]

The scores are local to the individual user, so unfortunately you can’t compare scores between users. You’re correct in what you said – those scores are only used for ranking items for user 0.