item-item similarity

I’m trying to implement both recommendations and item-item similarities based on an ImplicitFactorizationModel trained on clickstream data. The former is trivial using the predict method, but the latter I’m not quite sure about. Looking at the code, I could obtain the item embeddings in a similar way to how it’s done in _components.py and representations.py, and then apply a similarity measure to the resulting torch tensors.

Is this a sensible approach / Is there a better way to do this / Is it even possible?

[maciejkula]

I strongly recommend using a GPU for sequence models if you aren’t already. The PyTorch CPU implementation is quite slow.

I think that sequence models are definitely a better way to go. They are more flexible and allow you to easily incorporate new interactions as they come in, without retraining your model.

[rragundez]

@maciejkula it seems this discussion went quite off-track to the issue. So coming to the issue of retrieving item similarities, this could done by adding a method to BilinearNet since you are already holding the item_representations there, let’s say this method is _compute_item_similarities(item_ids, ..). Then in the actual model you would have another method like def item_similarities(items_ids) which calls self._net._compute_item_similarities(items_ids, ...) something like that.

The same could be done with the user representations for example to identify clusters of users, etc.

Thanks to your good code this shouldn’t be too difficult to implement. I could take care of this if you find it useful