Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-head Decoding
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.
691971446bfd5965c0fd3343
Yunkai Zhang
claimed_verified
2025-11-17T10:26:53.719Z
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Yunkai Zhang
zhykoties
user
zhykoties
691971446bfd5965c0fd3344
Qiang Zhang
claimed_verified
2026-02-13T09:39:18.364Z
6520c929e3419abdcf4827ff
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Qiang
zhangtemplar
user
zhangtemplar
691971446bfd5965c0fd3345
Feng
691971446bfd5965c0fd3346
Lin
691971446bfd5965c0fd3347
Ruizhong Qiu
691971446bfd5965c0fd3348
Hanchao Yu
691971446bfd5965c0fd3349
Jason Liu
691971446bfd5965c0fd334a
Yinglong Xia
691971446bfd5965c0fd334b
Zhuoran Yu
691971446bfd5965c0fd334c
Zeyu Zheng
691971446bfd5965c0fd334d
Diji Yang
claimed_verified
2026-01-12T10:37:39.161Z
62a17ca6d57bf9ab9c697206
/avatars/40cca602d481382b0adb13996328b515.svg
Diji Yang
dyang39
user
dyang39
Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-head Decoding
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.
A framework integrates human priors into end-to-end generative recommenders, enhancing accuracy and beyond-accuracy objectives by leveraging lightweight adapter heads and hierarchical composition strategies.
recommender systems diversity novelty personalization human priors item taxonomies temporal patterns end-to-end generative recommendation foundation models backbone-agnostic framework prior-conditioned adapter heads efficient LLM decoding strategies user intent interaction types hierarchical composition strategy