GateSID: Adaptive Gating for Balancing Semantic and Collaborative Signals in Recommendation

Abstract

In cold-start scenarios, the scarcity of collaborative signals for new items exacerbates the Matthew effect, undermining platform diversity and posing a persistent challenge in practice. Existing methods augment cold-start items' collaborative signals with semantic information, yet face a collaborative-semantic trade-off: collaborative signals work well for popular items but degrade on cold-start ones, while excessive reliance on semantics ignores collaborative differences. To address this, we propose GateSID, which introduces an adaptive gating network to dynamically balance semantic and collaborative signals based on item maturity. We first discretize multimodal features into hierarchical Semantic IDs (SID) via Residual Quantized VAE, then propose two components: (1) Gating-Fused Shared Attention (GFSA), which fuses attention distributions with gate-regulated weights; (2) Gate-Regulated Contrastive Alignment (GRCA), which enforces stronger alignment for cold-start items while relaxing it for popular ones. Experiments on large-scale industrial datasets demonstrate GateSID's superiority over competitive baselines, with the largest gains on popular items. An online A/B test confirms practical effectiveness: GMV +2.6%, CTR +1.1%, and Order +1.6%, with less than 5ms of additional latency. Beyond the method itself, we conduct a comprehensive exploration of SID in ranking models, systematically studying embedding types, SID configurations, and fusion strategies. We hope this exploration offers some useful insights for the community.