In cold-start scenarios, the scarcity of collaborative signals for new items exacerbates the Matthew effect, which undermines platform diversity and remains a persistent challenge in real-world recommender systems. Existing methods typically enhance collaborative signals with semantic information, but they often suffer from a collaborative-semantic tradeoff: collaborative signals are effective for popular items but unreliable for cold-start items, whereas over-reliance on semantic information may obscure meaningful collaborative differences. To address this issue, we propose GateSID, a framework that uses an adaptive gating network to dynamically balance semantic and collaborative signals according to item maturity. Specifically, we first discretize multimodal features into hierarchical Semantic IDs using Residual Quantized VAE. Building on this representation, we design two key components: (1) Gating-Fused Shared Attention, which fuses intra-modal attention distributions with item-level gating weights derived from embeddings and statistical features; and (2) Gate-Regulated Contrastive Alignment, which adaptively calibrates cross-modal alignment, enforcing stronger semantic-behavior consistency for cold-start items while relaxing the constraint for popular items to preserve reliable collaborative signals. Extensive offline experiments on large-scale industrial datasets demonstrate that GateSID consistently outperforms strong baselines. Online A/B tests further confirm its practical value, yielding +2.6% GMV, +1.1% CTR, and +1.6% orders with less than 5 ms additional latency.

翻译：在冷启动场景中，新物品协同信号的稀缺性加剧了马太效应，这削弱了平台多样性，并始终是真实推荐系统中的持续性挑战。现有方法通常利用语义信息增强协同信号，但常面临协同-语义权衡困境：协同信号对热门物品有效但对冷启动物品不可靠，而过度依赖语义信息可能掩盖有意义的协同差异。为解决此问题，我们提出GateSID框架，该框架通过自适应门控网络根据物品成熟度动态平衡语义信号与协同信号。具体而言，我们首先利用残差量化VAE将多模态特征离散化为分层语义ID。基于该表征，我们设计两个核心组件：（1）门控融合共享注意力——融合模态内注意力分布与基于嵌入和统计特征生成的物品级门控权重；（2）门控调节对比对齐——自适应校准跨模态对齐，对冷启动物品施加更强的语义-行为一致性约束，同时放松热门物品的约束以保留可靠协同信号。在大型工业数据集上的广泛离线实验表明，GateSID持续优于强基线模型。在线A/B测试进一步验证其实用价值，在额外延迟小于5毫秒条件下实现GMV+2.6%、CTR+1.1%及订单量+1.6%的提升。