不浪费：通过多头解码引导生成式推荐系统遵循结构化人类先验
Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-head Decoding
November 13, 2025
作者: Yunkai Zhang, Qiang Zhang, Feng, Lin, Ruizhong Qiu, Hanchao Yu, Jason Liu, Yinglong Xia, Zhuoran Yu, Zeyu Zheng, Diji Yang
cs.AI
摘要
优化推薦系統以實現超越準確性的目標（如多樣性、新穎性及個性化）對於提升長期用戶滿意度至關重要。為此，工業界實踐者已積累了大量結構化領域知識，我們稱之為人類先驗（例如物品分類體系、時序模式）。這類知識通常通過排名或後排名階段的後處理調整來應用，但這種方式始終與核心模型學習相分離——隨著行業向端到端生成式推薦基礎模型轉型，此種脫節尤為不利。另一方面，許多針對超越準確性目標的方法往往需要針對特定架構進行修改，並以完全無監督的方式學習用戶意圖，從而捨棄了這些寶貴的人類先驗知識。
我們提出了一種與基礎模型無關的框架，旨在將歷經多年實踐積累的人類先驗知識無縫整合到生成式推薦模型的端到端訓練中，而非將其捨棄。受高效大語言模型解碼策略啟發，我們通過輕量級的先驗條件適配頭，引導模型沿人類可理解的維度（如互動類型、長期與短期興趣）解耦用戶意圖。同時，針對不同先驗類型間的複雜交互，我們引入了層級化組合策略進行建模。在三個大規模數據集上的廣泛實驗表明，我們的方法能顯著提升準確性及超越準確性的各項指標。研究還證實，人類先驗知識可幫助基礎模型更有效地利用更長的上下文長度與更大的模型規模。
English
Optimizing recommender systems for objectives beyond accuracy, such as diversity, novelty, and personalization, is crucial for long-term user satisfaction. To this end, industrial practitioners have accumulated vast amounts of structured domain knowledge, which we term human priors (e.g., item taxonomies, temporal patterns). This knowledge is typically applied through post-hoc adjustments during ranking or post-ranking. However, this approach remains decoupled from the core model learning, which is particularly undesirable as the industry shifts to end-to-end generative recommendation foundation models. On the other hand, many methods targeting these beyond-accuracy objectives often require architecture-specific modifications and discard these valuable human priors by learning user intent in a fully unsupervised manner.
Instead of discarding the human priors accumulated over years of practice, we introduce a backbone-agnostic framework that seamlessly integrates these human priors directly into the end-to-end training of generative recommenders. With lightweight, prior-conditioned adapter heads inspired by efficient LLM decoding strategies, our approach guides the model to disentangle user intent along human-understandable axes (e.g., interaction types, long- vs. short-term interests). We also introduce a hierarchical composition strategy for modeling complex interactions across different prior types. Extensive experiments on three large-scale datasets demonstrate that our method significantly enhances both accuracy and beyond-accuracy objectives. We also show that human priors allow the backbone model to more effectively leverage longer context lengths and larger model sizes.