I built a recommendation model on a user-item transactional dataset where each transaction is represented by 1.
model = LightFM(learning_rate=0.05, loss='warp')
Here are the results
Train precision at k=3: 0.115301 Test precision at k=3: 0.0209936 Train auc score: 0.978294 Test auc score : 0.810757 Train recall at k=3: 0.238312330233 Test recall at k=3: 0.0621618086561
Can anyone help me interpret this result? How is it that I am getting such good auc score and such bad precision/recall? The precision/recall gets even worse for 'bpr' Bayesian personalized ranking.
users =  items = np.array([13433, 13434, 13435, 13436, 13437, 13438, 13439, 13440]) model.predict(users, item)
array([-1.45337546, -1.39952552, -1.44265926, -0.83335167, -0.52803332, -1.06252205, -1.45194077, -0.68543684])
How do I interpret the prediction scores?