MemoNet:Memorizing Representations of All Cross Features Efficiently via Multi-Hash Codebook Network for CTR Prediction. (arXiv:2211.01334v2 [cs.IR] UPDATED)

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