INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems

• URL: http://arxiv.org/abs/2508.11565v1
• Date: Fri, 15 Aug 2025 16:18:32 GMT
• Title: INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems
• Authors: Kaiyuan Li, Dongdong Mao, Yongxiang Tang, Yanhua Cheng, Yanxiang Zeng, Chao Wang, Xialong Liu, Peng Jiang,
• Abstract summary: Information Flow Network (INFNet) is a task-aware architecture designed for large-scale recommendation scenarios.<n>INFNet distinguishes features into three token types, categorical tokens, sequence tokens, and task tokens, and introduces a novel dual-flow design.<n>INFNet has been successfully deployed in a commercial online advertising system, yielding significant gains of +1.587% in Revenue (REV) and +1.155% in Click-Through Rate (CTR)
• Score: 8.283354901677692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature interaction has long been a cornerstone of ranking models in large-scale recommender systems due to its proven effectiveness in capturing complex dependencies among features. However, existing feature interaction strategies face two critical challenges in industrial applications: (1) The vast number of categorical and sequential features makes exhaustive interaction computationally prohibitive, often resulting in optimization difficulties. (2) Real-world recommender systems typically involve multiple prediction objectives, yet most current approaches apply feature interaction modules prior to the multi-task learning layers. This late-fusion design overlooks task-specific feature dependencies and inherently limits the capacity of multi-task modeling. To address these limitations, we propose the Information Flow Network (INFNet), a task-aware architecture designed for large-scale recommendation scenarios. INFNet distinguishes features into three token types, categorical tokens, sequence tokens, and task tokens, and introduces a novel dual-flow design comprising heterogeneous and homogeneous alternating information blocks. For heterogeneous information flow, we employ a cross-attention mechanism with proxy that facilitates efficient cross-modal token interaction with balanced computational cost. For homogeneous flow, we design type-specific Proxy Gated Units (PGUs) to enable fine-grained intra-type feature processing. Extensive experiments on multiple offline benchmarks confirm that INFNet achieves state-of-the-art performance. Moreover, INFNet has been successfully deployed in a commercial online advertising system, yielding significant gains of +1.587% in Revenue (REV) and +1.155% in Click-Through Rate (CTR).

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