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HiSAC framework enables efficient ultra-long sequence modeling for recommender systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Hierarchical Sparse Activation Compression (HiSAC), a new framework designed to make ultra-long sequence modeling feasible for recommender systems. HiSAC addresses limitations in existing methods by encoding interactions into multi-level semantic IDs and using a hierarchical voting mechanism to activate personalized interest-agents. This approach allows for fine-grained preference centers and uses Soft-Routing Attention to aggregate historical signals efficiently, minimizing quantization errors and preserving long-tail preferences. When deployed on Taobao's "Guess What You Like" feature, HiSAC demonstrated significant compression and cost reduction, leading to a 1.65% increase in click-through rate. AI

IMPACT Enables more efficient and personalized recommendations by handling longer user behavior sequences.

RANK_REASON Publication of a research paper on a novel framework for sequence modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

HiSAC framework enables efficient ultra-long sequence modeling for recommender systems

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Publication of a research paper on a novel framework for sequence modeling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Kun Yuan, Junyu Bi, Daixuan Cheng, Changfa Wu, Shuwen Xiao, Binbin Cao, Jian Wu, Yuning Jiang ·

    HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders

    arXiv:2602.21009v2 Announce Type: replace-cross Abstract: Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory constraints. While summarizing history vi…

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