{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discrete-semantic-tokenization-for-deep-ctr","title":"Discrete Semantic Tokenization for Deep CTR Prediction","arxiv_id":"2403.08206","date":"2024-03-13","proceeding":null,"authors":["Qijiong Liu","Hengchang Hu","Jiahao Wu","Jieming Zhu","Min-Yen Kan","Xiao-Ming Wu"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2403.08206v2","url_pdf":"https://arxiv.org/pdf/2403.08206v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"discrete-semantic-tokenization-for-deep-ctr","repo_url":"https://github.com/jyonn/semantictokenizer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"discrete-semantic-tokenization-for-deep-ctr","repo_url":"https://github.com/jyonn/legommenders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"news-recommendation","task_name":"News Recommendation"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}