Discrete Semantic Tokenization for Deep CTR Prediction
<|reference_start|>Discrete Semantic Tokenization for Deep CTR Prediction: Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios. The content-encoding paradigm, which integrates user and item encoders directly into CTR models, prioritizes space over time. In contrast, the embedding-based paradigm transforms item and user semantics into latent embeddings, subsequently caching them to optimize processing time at the expense of space. In this paper, we introduce a new semantic-token paradigm and propose a discrete semantic tokenization approach, namely UIST, for user and item representation. UIST facilitates swift training and inference while maintaining a conservative memory footprint. Specifically, UIST quantizes dense embedding vectors into discrete tokens with shorter lengths and employs a hierarchical mixture inference module to weigh the contribution of each user--item token pair. Our experimental results on news recommendation showcase the effectiveness and efficiency (about 200-fold space compression) of UIST for CTR prediction.<|reference_end|>
@article{liu2024discrete,
    title={Discrete Semantic Tokenization for Deep CTR Prediction},
    author={Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu, Min-Yen Kan,
  Xiao-Ming Wu},
    journal={arXiv preprint arXiv:2403.08206},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08206},
    primaryClass={cs.IR}
}