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New findings in natural language processing (NLP) demonstrate that the strongmemorization capability contributes a lot to the success of Large LanguageModels (LLM). This inspires us to explicitly bring an independent memorymechanism 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 representationsof cross features in CTR tasks. HCNet uses a multi-hash codebook as the mainmemory place and the whole memory procedure consists of three phases:multi-hash addressing, memory restoring, and feature shrinking. We also proposea new CTR model named MemoNet which combines HCNet with a DNN backbone.Extensive experimental results on three public datasets and online test showthat MemoNet reaches superior performance over state-of-the-art approaches.Besides, MemoNet shows scaling law of large language model in NLP, which meanswe can enlarge the size of the codebook in HCNet to sustainably obtainperformance gains. Our work demonstrates the importance and feasibility oflearning and memorizing representations of cross features, which sheds light ona new promising research direction.

MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction