lyst / lyst/lightfm

Predict using user features

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Dominant language
Python
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Description

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)
`

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First steps

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Research direction

Start by reading the documented predict and predict_rank entry points and their user_ids and user_features parameters. Review the existing issue discussion for constraints and determine the expected behavior for feature-only prediction; done means an agreed API design with corresponding implementation and tests.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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