Patrick Orlando

@OmarMAmin I can't find the section you quoted in that paper, and I don't quite follow your calculation. I'll walk you through my understanding. Firstly, the section is describing how...

Interesting @OmarMAmin, I've read the section and as far as I can tell, it does seem to estimate the frequency in the way you've described. It's not really aligned with...

Yeah I can't find any specifics in this paper @hkristof03, but I've seen the use of scaled dot-product attention (Luong Style) in other papers like [SASrec](https://arxiv.org/pdf/1808.09781.pdf). The inputs must be...

Perhaps @ydennisy, you could calculate the mean and variance of your batch of scores and calculate a loss based on your desired mean and variance. You could add this into...

@kapilduhoon you must concatenate them to your candidate embeddings tensor on the first axis. If you have B batch size, D dimensional output embeddings and K extra negatives, then you...

In this case I would advise you to manually trace the data through your model components. This should help you identify clearly where the tensors don't match what you should...

You've got a couple of errors in your implementation. 1. Not indexing the model correctly. `index_from_dataset` expects a tuple of `(item_id, item_embedding)`. From the [tutorial](https://www.tensorflow.org/recommenders/examples/efficient_serving#approximate_prediction): ```python brute_force = tfrs.layers.factorized_top_k.BruteForce(model.user_model) brute_force.index_from_dataset(...

Hi @IgorHoholko, When you say training speed, do you mean time per step or number of steps required for loss to reach a certain level? For your second point there...

Then that is quite odd. Are you using multiple GPUs @IgorHoholko?

Hey @datasciyj, Your ranking model dataset only contains positive examples. A classifier that predicts 1 for any input would achieve a perfect score. You need to have negative examples in...