Query-Mixed Interest Extraction and Heterogeneous Interaction: A Scalable CTR Model for Industrial Recommender Systems

• URL: http://arxiv.org/abs/2602.09387v2
• Date: Thu, 12 Feb 2026 11:52:56 GMT
• Title: Query-Mixed Interest Extraction and Heterogeneous Interaction: A Scalable CTR Model for Industrial Recommender Systems
• Authors: Fangye Wang, Guowei Yang, Xiaojiang Zhou, Song Yang, Pengjie Wang,
• Abstract summary: HeMix is a scalable ranking model that unifies adaptive sequence tokenization and heterogeneous interaction structure.<n>HeMix is deployed on the AMAP platform, delivering significant online gains over DLRM: +3.61% GMV, +2.78% PV_CTR, and +2.12% UV_CVR.
• Score: 6.312847671238921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning effective feature interactions is central to modern recommender systems, yet remains challenging in industrial settings due to sparse multi-field inputs and ultra-long user behavior sequences. While recent scaling efforts have improved model capacity, they often fail to construct both context-aware and context-independent user intent from the long-term and real-time behavior sequence. Meanwhile, recent work also suffers from inefficient and homogeneous interaction mechanisms, leading to suboptimal prediction performance. To address these limitations, we propose HeMix, a scalable ranking model that unifies adaptive sequence tokenization and heterogeneous interaction structure. Specifically, HeMix introduces a Query-Mixed Interest Extraction module that jointly models context-aware and context-independent user interests via dynamic and fixed queries over global and real-time behavior sequences. For interaction, we replace self-attention with the HeteroMixer block, enabling efficient, multi-granularity cross-feature interactions that adopt the multi-head token fusion, heterogeneous interaction and group-aligned reconstruction pipelines. HeMix demonstrates favorable scaling behavior, driven by the HeteroMixer block, where increasing model scale via parameter expansion leads to steady improvements in recommendation accuracy. Experiments on industrial-scale datasets show that HeMix scales effectively and consistently outperforms strong baselines. Most importantly, HeMix has been deployed on the AMAP platform, delivering significant online gains over DLRM: +3.61\% GMV, +2.78\% PV\_CTR, and +2.12\% UV\_CVR.

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