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I am going to build a recommender system using TensorFlow recommender and the two-tower-model. I have wondered, how to choose the size of the embedding dimension. Are there any papers on this for large scale recommender systems? For the example, Google chose a size of 32 dimensions for the movie recommender. My vocabulary contains around 30,000 different items.

Help is highly appreciated!

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Generally, the size of the embedding layer is not an important hyperparameter.

Research has found embeddings dimension lower than ~19 are not performant and there is asymptotic improvement after ~200 dimensions.

Multiples of 32 are chosen for hardware efficiency.

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