GateSID: Adaptive Gating for Semantic-Collaborative Alignment in Cold-Start Recommendation

TL;DR

GateSID introduces an adaptive gating framework that dynamically balances semantic and collaborative signals in cold-start recommendation, improving diversity and accuracy in large-scale industrial systems.

Contribution

The paper proposes a novel adaptive gating network with hierarchical Semantic IDs and cross-modal alignment to enhance cold-start recommendation performance.

Findings

Outperforms strong baselines in offline experiments

Achieves +2.6% GMV, +1.1% CTR, +1.6% orders in online A/B tests

Maintains low latency with less than 5 ms additional delay

Abstract

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.