SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation

• URL: http://arxiv.org/abs/2508.01375v1
• Date: Sat, 02 Aug 2025 14:09:21 GMT
• Title: SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation
• Authors: Yining Yao, Ziwei Li, Shuwen Xiao, Boya Du, Jialin Zhu, Junjun Zheng, Xiangheng Kong, Yuning Jiang,
• Abstract summary: We propose a Semantic-Behavior Alignment for Cold-start Recommendation framework.<n>First, we leverage domain-specific knowledge to train a multimodal encoder to generate semantic representations.<n>Second, we use residual quantized semantic ID to dynamically bridge the gap between multimodal representations and the ranking model.
• Score: 14.449201436664692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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