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hello everyone iamp;#39;m xian zhang from michigan state university thanks for attending this talk about field aware embedding dimension search in recommended systems this is a joint work with linkedin and gd.com so in real water recommended systems they usually contains hundreds of feature fields these fields come from users item contextual information and their interactions so deeply augmented systems typically transform these features into embedding vectors however in most existing systems they assign the same embedding dimension to all feature fields i think this will use the embedding memory inefficiently i think there are two reasons one is actually an embeddingamp;#39;s dimension often decides its ability to capture information so if we simply assign the same dimension to all feature fields we may lose the information of important features but this memory are unimportant for example the location feature is very important in location-based recommended system so we should assign
