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TensorFlow Recommenders is a library for building recommender system models using TensorFlow.

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The bug is summarized by the following issue comment. https://github.com/tensorflow/recommenders/issues/712#issuecomment-2041163592

Sorry if this may be the wrong place to ask, I couldn't find a lead anywhere after weeks of googling. I've implemented a retrieval model to create embeddings for cosine...

session 1. def build_model(): model = RetrievalModel(item_model, user_model) learning_rate = 0.01 model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate)) return model model = build_model() model.fit(behavior_dataset, epochs=30) model.save_weights(save_path,overwrite=True ) compute_loss_args = { "user_id" : tf.constant(["45"]), "work_id" : tf.constant(["45"]),...

I have tried a two-tower model (user and query) in a real industrial scenario using contrastive learning. The samples are all actual click samples, and the loss function is InfoNCE....

The current function `sample_listwise` supports a limited number of features that are hardcoded in the function definition. This pull requests makes it easy for other features to be used as...

Hi, as the title says, the performance of the model drops when I use a cluster of GPUs. The (custom) training is being done in vertex training service. This is...

Hi, I'm trying to build a retrieval model. I have positive samples and some weights corresponding to each of these sample which helps to denote that for the given sample...

Hi, I have been working through the Recommending movies: retrieval tutorial and in the Metrics portion of the jupyter notebook I have been receiving a ValueError: Cannot convert '('c', 'o',...

Getting below error when trying Quickstart example. --------------- > File "/Users/terry/Documents/project/recommender/recommenders/collab_filtering_movie.py", line 245, in > index.index_from_dataset( > File "/Users/terry/Documents/project/recommender/recommenders/tensorflow_recommenders/layers/factorized_top_k.py", line 215, in index_from_dataset > return self.index(candidates, identifiers) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ >...