Patrick Orlando

It's perfectly fine to only use a retrieval model, it also simplifies getting your model into production and reduces response latency. There are advantages to ranking models, particularly if you...

@Ullar-Kask, I don't know of any rule of thumb here. I think it's something you'd need to experiment with. It probably depends on the number of items in your catalogue...

Hi @hkristof03, 1. As with many things in recsys, it depends. For example if seasonality or trends affect the relevance of items then your model may perform better with a...

@OmarMAmin, This is true in the case where a `log(q)` correction is not applied. It turns out that the bias introduced by using in-batch negative sampling can be accounted for...

I'm glad I could help @OmarMAmin 😁 That sounds like a great idea.

Neural Collaborative Filtering is a class of embedding factorization models where the similarity function between the user and item embedding is learned, (usually by an MLP), as opposed to being...

If the model uses a learnable layer to calculate the similarity/relevance score, then it may be considered an NCF model, but I wouldn't focus too much on this terminology. The...

@OmarMAmin, @jillwalker99 is correct, you may choose to implement a ranking only model provided serving time and cost is within budget. There is one other benefit to having a dot-product...

@jillwalker99 The concept of the two-tower recommender model is closely related to the Dual Encoder in Information Retrieval literature. Here's some papers that might be of interest: - [End-to-End Retrieval...

1. It is calculated in the retrieval task, https://github.com/tensorflow/recommenders/blob/7caed557b9d5194202d8323f2d4795231a5d0b1d/tensorflow_recommenders/tasks/retrieval.py#L160-L161 2. It is modelled as a massive multi class classification problem. Every candidate is a class. However candidates are sampled in...