  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.
