SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation

2025-08-02

AI Summary

To address the challenges of semantic-behavioral space misalignment and insufficient model lightweighting in CTR prediction for cold-start and long-tail items, this paper proposes a Semantic-Behavioral Dynamic Alignment (SBDA) framework. Our method introduces two key innovations: (1) a domain-knowledge-enhanced, behavior-aware multimodal encoder that aligns semantic representations with user behavioral distributions; and (2) a residual quantized semantic ID mechanism—a lightweight bridge between multimodal features and ranking models—enabling efficient, continual alignment. Extensive experiments on the Taobao platform demonstrate significant improvements: offline AUC increases by 0.83%, while online CTR and order volume rise by 13.21% and 13.44%, respectively. These gains substantially enhance recommendation performance for cold-start and long-tail items.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In recommendation systems, predicting Click-Through Rate (CTR) is crucial for accurately matching users with items. To improve recommendation performance for cold-start and long-tail items, recent studies focus on leveraging item multimodal features to model users' interests. However, obtaining multimodal representations for items relies on complex pre-trained encoders, which incurs unacceptable computation cost to train jointly with downstream ranking models. Therefore, it is important to maintain alignment between semantic and behavior space in a lightweight way. To address these challenges, we propose a Semantic-Behavior Alignment for Cold-start Recommendation framework, which mainly focuses on utilizing multimodal representations that align with the user behavior space to predict CTR. First, we leverage domain-specific knowledge to train a multimodal encoder to generate behavior-aware semantic representations. Second, we use residual quantized semantic ID to dynamically bridge the gap between multimodal representations and the ranking model, facilitating the continuous semantic-behavior alignment. We conduct our offline and online experiments on the Taobao, one of the world's largest e-commerce platforms, and have achieved an increase of 0.83% in offline AUC, 13.21% clicks increase and 13.44% orders increase in the online A/B test, emphasizing the efficacy of our method.

Problem

• Improving cold-start item recommendations efficiently
• Aligning semantic and behavior spaces cost-effectively
• Reducing computation costs in multimodal recommendation systems

Innovation

• Behavior-aware multimodal encoder training
• Residual quantized semantic ID bridging
• Lightweight semantic-behavior alignment framework