DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System

• URL: http://arxiv.org/abs/2508.10584v1
• Date: Thu, 14 Aug 2025 12:22:51 GMT
• Title: DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
• Authors: Wencai Ye, Mingjie Sun, Shaoyun Shi, Peng Wang, Wenjin Wu, Peng Jiang,
• Abstract summary: We propose a one-stage Dual-Aligned Semantic IDs (DAS) method that simultaneously optimize quantization and alignment.<n>DAS achieves more efficient alignment between the semantic IDs and collaborative signals, with the following two innovative approaches.<n>DAS is successfully deployed across various advertising scenarios at Kuaishou App, serving over 400 million users daily.
• Score: 15.648601380538413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic IDs are discrete identifiers generated by quantizing the Multi-modal Large Language Models (MLLMs) embeddings, enabling efficient multi-modal content integration in recommendation systems. However, their lack of collaborative signals results in a misalignment with downstream discriminative and generative recommendation objectives. Recent studies have introduced various alignment mechanisms to address this problem, but their two-stage framework design still leads to two main limitations: (1) inevitable information loss during alignment, and (2) inflexibility in applying adaptive alignment strategies, consequently constraining the mutual information maximization during the alignment process. To address these limitations, we propose a novel and flexible one-stage Dual-Aligned Semantic IDs (DAS) method that simultaneously optimizes quantization and alignment, preserving semantic integrity and alignment quality while avoiding the information loss typically associated with two-stage methods. Meanwhile, DAS achieves more efficient alignment between the semantic IDs and collaborative signals, with the following two innovative and effective approaches: (1) Multi-view Constrative Alignment: To maximize mutual information between semantic IDs and collaborative signals, we first incorporate an ID-based CF debias module, and then design three effective contrastive alignment methods: dual user-to-item (u2i), dual item-to-item/user-to-user (i2i/u2u), and dual co-occurrence item-to-item/user-to-user (i2i/u2u). (2) Dual Learning: By aligning the dual quantizations of users and ads, the constructed semantic IDs for users and ads achieve stronger alignment. Finally, we conduct extensive offline experiments and online A/B tests to evaluate DAS's effectiveness, which is now successfully deployed across various advertising scenarios at Kuaishou App, serving over 400 million users daily.

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