View article

Deep learning-based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimension of categorical variables (e.g., user/item identifiers) and meaningfully transform them in the low-dimensional space. The majority of existing DLRSs empirically pre-define a fixed and unified dimension for all user/item embeddings. It is evident from recent researches that different embedding sizes are highly desired for different users/items according to their frequency. However, manually selecting embedding sizes in recommender systems can be very challenging due to a large number of users/items and the dynamic nature of their frequency. Thus, in this paper, we propose an AutoML based end-to-end framework (AutoEmb), enabling various embedding dimensions according to the frequency in an automated and dynamic manner. To be specific, we first enhance a typical DLRS to allow …