Dealing with Missing Item Features

[keryums]

Hello,

How does this package deal with the case of passing in item features for fewer items than is in the user-item utility matrix? We've noticed that the model will train and we can extract item representations from the trained model even without explicitly imputing for these missing item features.

Thanks

[SimonCW]

Hi @keryums

maybe this excerpt from the notes in the documentation is helpful:

If no feature matrices are provided to the lightfm.LightFM.fit() or lightfm.LightFM.predict() methods, they are implicitly assumed to be identity matrices: that is, each user and item are characterised by one feature that is unique to that user (or item). In this case, LightFM reduces to a traditional collaborative filtering matrix factorization method. https://making.lyst.com/lightfm/docs/lightfm.html

If you pass in features to the fit calls and you have used the built-in dataset construction, than adding one "identity feature" per user and item is also the default. See: https://making.lyst.com/lightfm/docs/lightfm.data.html#lightfm.data.Dataset

I hope this helps!

If this doesn't clear things up, maybe you could provide a concrete example, e.g. shape of your matrices, way of constructing them.

[keryums]

Thank you that makes sense!

Ker-Yu 415-312-2148

On Wed, Dec 30, 2020 at 4:22 PM Simon Weiß notifications@github.com wrote:

Hi @keryums https://github.com/keryums

maybe this excerpt from the notes in the documentation is helpful:

If no feature matrices are provided to the lightfm.LightFM.fit() or lightfm.LightFM.predict() methods, they are implicitly assumed to be identity matrices: that is, each user and item are characterised by one feature that is unique to that user (or item). In this case, LightFM reduces to a traditional collaborative filtering matrix factorization method. https://making.lyst.com/lightfm/docs/lightfm.html

If you pass in features to the fit calls and you have used the built-in dataset construction, than adding one "identity feature" per user and item is also the default. See: https://making.lyst.com/lightfm/docs/lightfm.data.html#lightfm.data.Dataset

If this doesn't clear things up, maybe you could provide a concrete example, e.g. shape of your matrices, way of constructing them.

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[SimonCW]

Perfect, then I'll close.