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Thanks for your thoughts! We do not have data about what was visible for the user each time on the page, nor the click data. As a solution to the...

The approach works as our testing shows. We generate N negative samples for each positive sample (as mentioned above). The larger the value of N the better results. Currently we...

Your `products` need to have the same structure as inputs to the `candidate_model`, i.e. ``` { "title":features["LINE_DESC"], "int":features["int"], } ```

The model becomes compilable if you fix the error with `products` as mentioned above, e.g. `products = product.map(lambda x: {"titles": x["LINE_DESC"], "int": x["LINE_NUMBER"]},num_parallel_calls=tf.data.AUTOTUNE)` And then you need to fix the...

Hi @patrickorlando, > You could try randomly sampling k negatives per positive example What is the reasonable value of k ? Thanks!

Hi, 1. I think it does. But how do you calculate it per user?