Predict using user features

Hello, I would like to ask how we can predict using user features instead of user ids. Because having user ids means that lightFM needs to train with that user first. However, if I use user features, I can use training data of user with similar features, and recommend similar items.

Both predict and predict_rank have require use ids. Is there any way I can use user features to predict instead? predict(user_ids, item_ids, item_features=None, user_features=None, num_threads=1) predict_rank(test_interactions, train_interactions=None, item_features=None, user_features=None, num_threads=1)

[maciejkula]

You’re close. Remember, user and item ids are indices into their respective feature matrices. In this case, your item matrix has three rows, so you’d need to do:

predict(0, item_ids=[0, 1, 2], item_features=[11's features, 12's features, 13's features], user_features=[D.features])

[maciejkula]

1. It is usually better to provide it, because then the model will be more expressive: it will be able to express preference for every individual user. But sometimes this is counterproductive (data is too sparse) or impossible: you want to always predict for new users for whom you have no historical data (cold start).
2. Given the same features, predictions will be the same. It doesn’t matter which row it’s in: all that matters are the features in the row. If you use the identity matrix, this is of course impossible: every row is different.

Hope this helps!