MemoNet:Memorizing Representations of All Cross Features Efficiently via Multi-Hash Codebook Network for CTR Prediction. (arXiv:2211.01334v1 [cs.IR])

New findings in natural language processing(NLP) demonstrate that the strong
memorization capability contributes a lot to the success of large language
models.This inspires us to explicitly bring an independent memory mechanism
into CTR ranking model to learn and memorize all cross features'
representations.In this paper,we propose multi-Hash Codebook NETwork(HCNet) as
the memory mechanism for efficiently learning and memorizing representations of
all cross features in CTR tasks.HCNet uses multi-hash codebook as the main
memory place and the whole memory procedure consists …

arxiv features hash network prediction