  New findings in natural language processing (NLP) demonstrate that the strong
memorization capability contributes a lot to the success of Large Language
Models (LLM). This inspires us to explicitly bring an independent memory
mechanism into CTR ranking model to learn and memorize cross features'
representations. In this paper, we propose multi-Hash Codebook NETwork (HCNet)
as the memory mechanism for efficiently learning and memorizing representations
of cross features in CTR tasks. HCNet uses a multi-hash codebook as the main
memory place and the whole memory procedure consists of three phases:
multi-hash addressing, memory restoring, and feature shrinking. We also propose
a new CTR model named MemoNet which combines HCNet with a DNN backbone.
Extensive experimental results on three public datasets and online test show
that MemoNet reaches superior performance over state-of-the-art approaches.
Besides, MemoNet shows scaling law of large language model in NLP, which means
we can enlarge the size of the codebook in HCNet to sustainably obtain
performance gains. Our work demonstrates the importance and feasibility of
learning and memorizing representations of cross features, which sheds light on
a new promising research direction.
