Cubic_Gate

We can test the improvement of the Rank for a few users. Say, user 42, he has 183 watched movies. We can let the Retrieval to recommend 1000 movies for...

Thanks @rlcauvin I agree with you. Is it possible to have a demonstration of using the actual recommended movies as a way to measure the improvement of the Rank from...

The examples in the tutorials such as Multi-task recommenders can clearly show their comparison. But it seems to me the advantage of using Rank + Retrieval is not clearly shown...

Thanks @rlcauvin for your advice! I am curious to learn if better evaluation metrics for Retrieval or Rank will translate into better actual movie recommendations? Do we have examples to...

Thanks @rlcauvin For your own content recommendations models, do you see the Rank improves the output of the Retrieval in the actual recommendations (not the metrics for evaluation)?

@rlcauvin Yes, this is my concern. I try not to have this case: Say, user 42, he has 183 watched movies. We can let the Retrieval recommend 1000 movies for...

Thanks @rlcauvin for this tip! Currently, my interest is to see if the recommendations can include the watched movies so we can have the "ground truth" to evaluate the quality...

Thank you @rlcauvin and @patrickorlando for advising me to use these metrics to evaluate the Rank. I am curious if the tutorials of Tensorflow Recommders can provide such examples so...

Hi @maciejkula I tried your idea: model.retrieval_task.factorized_metrics = ( tfrs.metrics.FactorizedTopK( candidates=tfrs.layers.factorized_top_k.BruteForce().index(items_ds.batch(8192).map(self.item_model) ) ) with the lines from the retrieval example: me = movies.batch(8192).map(movie_model) candidates=tfrs.layers.factorized_top_k.BruteForce().index(me) task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK( # movies.batch(128).map(movie_model) #...

Hi @patrickorlando The following lines are usually used during inference after the model is trained: brute_force = tfrs.layers.factorized_top_k.BruteForce(model.user_model) brute_force.index_from_dataset( movies.batch(128).map(lambda title: (title, model.movie_model(title))) ) However, the idea of @maciejkula model.retrieval_task.factorized_metrics...