HyTRec：一種用於長行為序列推薦的混合時序感知注意力架構

摘要

對使用者行為長序列建模已成為生成式推薦領域的關鍵前沿。然而現有解決方案面臨兩難困境：線性注意力機制雖能提升效率，卻因狀態容量限制而犧牲檢索精度；軟注意力則存在難以承受的計算開銷。為解決此難題，我們提出HyTRec模型，其混合注意力架構能顯式解耦長期穩定偏好與短期意圖峰值。通過將海量歷史序列分配至線性注意力分支，並為近期互動保留專用軟注意力分支，我們的方案在涉及萬級互動的工業級場景中恢復了精確檢索能力。為緩解線性層捕捉快速興趣漂移的滯後性，我們進一步設計時序感知增量網絡，動態增強新近行為信號的權重，同時有效抑制歷史噪聲。工業級數據集上的實證結果驗證了模型優勢：在保持線性推理速度的同時超越強基線模型，對超長序列使用者的命中率提升超過8%，且具備卓越效率。

English

Modeling long sequences of user behaviors has emerged as a critical frontier in generative recommendation. However, existing solutions face a dilemma: linear attention mechanisms achieve efficiency at the cost of retrieval precision due to limited state capacity, while softmax attention suffers from prohibitive computational overhead. To address this challenge, we propose HyTRec, a model featuring a Hybrid Attention architecture that explicitly decouples long-term stable preferences from short-term intent spikes. By assigning massive historical sequences to a linear attention branch and reserving a specialized softmax attention branch for recent interactions, our approach restores precise retrieval capabilities within industrial-scale contexts involving ten thousand interactions. To mitigate the lag in capturing rapid interest drifts within the linear layers, we furthermore design Temporal-Aware Delta Network (TADN) to dynamically upweight fresh behavioral signals while effectively suppressing historical noise. Empirical results on industrial-scale datasets confirm the superiority that our model maintains linear inference speed and outperforms strong baselines, notably delivering over 8% improvement in Hit Rate for users with ultra-long sequences with great efficiency.

HyTRec：一種用於長行為序列推薦的混合時序感知注意力架構

HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation

摘要

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