Added

• added support for sample weights - you can now pass importance weights in addition to interactions
• automatically determine the input data class (np.ndarray vs. pd.dataframe/pd.series)
• assert/ensure that all model weights are finite after each training epoch to fail fast for exploding weights

Fixed

• bug where pd.dataframe interactions with columns not named [user_id, item_id] were not getting loaded/indexed correctly - fixed by using the input class determination utility created

Changed

• more efficient loops for updating item feature and user/item feature factor weights - this cuts training time by around 30% with no auxiliary features, and by 50%+ in the presence of auxiliary features