# DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System

arXiv: https://arxiv.org/abs/2508.10584 · HTML: https://mlsanity.com/paper/2508.10584/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2025.

## Built on by (most cited first, 8 of 8)

- [Stop Treating Collisions Equally: Qualification-Aware Semantic ID Learning for Recommendation at Industrial Scale](https://mlsanity.com/paper/2603.00632/) (2026)
- [When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Study](https://mlsanity.com/paper/2601.14697/) (2026)
- [ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation](https://mlsanity.com/paper/2606.14260/) (2026)
- [Multimodal Generative Retrieval Model with Staged Pretraining for Food Delivery on Meituan](https://mlsanity.com/paper/2602.06654/) (2026)
- [UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence](https://mlsanity.com/paper/2605.09040/) (2026)
- [FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation](https://mlsanity.com/paper/2605.21832/) (2026)
- [LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendation](https://mlsanity.com/paper/2607.24617/) (2026)
- [FedCGR: Federated Cross-Domain Generative Recommendation](https://mlsanity.com/paper/2608.10929/) (2026)

## Abstract (arXiv)

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.