Problem
Research questions and friction points this paper is trying to address.
feature interaction
heterogeneous tokens
parameter-inefficient
HubMixer: Progressive Latent Hub Mixing for Parameter-Efficient Feature Interaction in Recommendation
🤖 AI Summary
为解决推荐系统中特征交互参数效率低的问题，提出HubMixer架构，通过引入可学习的潜在枢纽来组织特征交互，提高模型性能。
📝 Abstract
Learning effective feature interactions is central to industrial recommendation and advertising ranking systems. Recent token-mixing architectures simplify self-attention with lightweight mixing operators, improving hardware efficiency and enabling large-scale deployment. However, recommendation tokens are fundamentally heterogeneous: user profiles, item attributes, behavioral sequences, context features, statistical signals, and business-side features live in different semantic spaces and interact in sparse, sample-specific patterns. Directly mixing all tokens in the raw heterogeneous token space may therefore be parameter-inefficient, as the model must implicitly discover which feature groups should interact and how such interactions should be routed. In the paper, we propose HubMixer, a parameter-efficient latent hub mixing architecture for feature interaction in recommendation. Instead of directly mixing raw feature tokens, HubMixer introduces a small set of learnable latent hubs to organize feature interactions through an `induction--interaction--readout` paradigm. First, hub induction summarizes heterogeneous tokens into compact latent hubs, where latent hubs query input tokens through cross-attention. Second, hub interaction performs high-order interaction in the cleaner latent hub space. Third, token-conditioned readout lets each original token selectively read from the interacted hubs, injecting global interaction semantics while preserving token-level field identity. Extensive offline experiments on industrial recommendation tasks show that HubMixer outperforms the SOTA models. Online A/B testing in the Kuaishou short-video recruitment business further shows a statistically significant 5.48% improvement in resume submission conversion rate, and HubMixer has been fully deployed in production.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Latent Hub Mixing
Parameter-Efficient
Feature Interaction
Induction-Interaction-Readout
Recommendation Systems
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