solved with cross-validation or other hyperparameter tuning techniques. In CTR prediction tasks we are predicting the event-probability of a click, which can also be viewed as context-based CF with binary ratings. A wide variety of models has been developed over the recent years for this task, including but not limited to [@cheng2016wide; @guo2017deepfm; @lian2018xdeepfm; @naumov2019deep; @zhou2018deepi; @zhou2018deep]. These state-of-the-art models share many similar characteristics, and all of them without exception use memory-intensive embedding layers that dwarf the rest of the model.

In modern machine learning, embeddings are often used to represent categorical features. Embedding vectors are mined from data, with the intention that certain semantic relationships between the categorical concepts represented by the vectors are encoded by the spatial or geometric relationships and properties of the vectors [@Mikolov2013distributed; @naumov2019dimensionality]. Thus, large embeddings are a natural choice for recommendation systems, which require models to understand the relationships between users and items.

Many techniques have been developed to decrease the amount of memory consumed by embedding layers. They can roughly be split into two high-level classes: (i) compression algorithms and (ii) compressed architectures. Compression algorithms usually involve some form of additional processing of the model beyond standard training. They can be performed 