Big impact from item features


I’m using Pinsage implementation from dgl. I’ve got item features (image embeddings) that I’m using as a FloatTensor (node features) in a similar way like that:

I saw that they have a big impact in that line, especially when they weren’t normalized:

I think that item features have a bigger impact than IDs embeddings. Should I change something to reduce impact from image embeddings and base more on interactions (user - item)? I think that in a recommendation system it could be a better idea.

Best regards

Whether to reduce the impact mostly depends on your dataset and scenario; sometimes image features can be quite discriminative and sometimes it may be not. That being said, how did you measure the “impact”? Did you compare the model performance between adding and not adding the image features? If so, you should handle features the way you get better performance.

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