Yintao Ren

Da Guo, Shijia Wang, Qiang Xiao et al.

Generative retrieval has emerged as a promising paradigm in recommender systems, offering superior sequence modeling capabilities over traditional dual-tower architectures. However, in large-scale industrial scenarios, such models often suffer from inherent myopia: due to single-step inference and strict latency constraints, they tend to collapse diverse user intents into locally optimal predictions, failing to capture long-horizon and multi-item consumption patterns. Moreover, real-world retrieval systems must follow explicit retrieval instructions, such as category-level control and policy constraints. Incorporating such instruction-following behavior into generative retrieval remains challenging, as existing conditioning or post-hoc filtering approaches often compromise relevance or efficiency. In this work, we present Climber-Pilot, a unified generative retrieval framework to address both limitations. First, we introduce Time-Aware Multi-Item Prediction (TAMIP), a novel training paradigm designed to mitigate inherent myopia in generative retrieval. By distilling long-horizon, multi-item foresight into model parameters through time-aware masking, TAMIP alleviates locally optimal predictions while preserving efficient single-step inference. Second, to support flexible instruction-following retrieval, we propose Condition-Guided Sparse Attention (CGSA), which incorporates business constraints directly into the generative process via sparse attention, without introducing additional inference steps. Extensive offline experiments and online A/B testing at NetEase Cloud Music, one of the largest music streaming platforms, demonstrate that Climber-Pilot significantly outperforms state-of-the-art baselines, achieving a 4.24\% lift of the core business metric.

Peng Liu, Cong Xu, Ming Zhao et al.

As the last pivotal stage of Recommender System (RS), Multi-Task Fusion (MTF) is responsible for combining multiple scores outputted by Multi-Task Learning (MTL) model into a final score to maximize user satisfaction. Recently, to optimize long-term user satisfaction, Reinforcement Learning (RL) is used for MTF in RSs. However, the existing offline RL algorithms used for MTF have the following severe problems: a) To avoid Out-of-Distribution (OOD), their constraints are overly strict, which seriously damage performance; b) They are unaware of the exploration policy used to collect training data, only suboptimal policy can be learned; c) Their exploration policies are inefficient and hurt user experience. To solve the above problems, we propose an innovative method called UnifiedRL tailored for MTF in large-scale RSs. UnifiedRL seamlessly integrates offline RL model with its custom exploration policy to relax overly strict constraints, which is different from existing RL-MTF methods and significantly improves performance. In addition, compared to existing exploration policies, UnifiedRL's custom exploration policy is highly efficient, enabling frequent online exploration and offline training iterations, which further improves performance. Extensive offline and online experiments are conducted in a large-scale RS. The results demonstrate that UnifiedRL outperforms other existing MTF methods remarkably, achieving a +4.64% increase in user valid consumption and a +1.74% increase in user duration time. To the best of our knowledge, UnifiedRL is the first RL algorithm tailored for MTF in RSs and has been successfully deployed in multiple large-scale RSs since June 2023, yielding significant benefits.