Once Generated, Ranked: End-to-End Generative Slate Recommendation with Unified Semantic-Collaborative IDs

Slate recommendation treats a slate rather than an individual item as the recommendation unit, requiring joint optimization of item interactions and slate utility. Existing approaches typically separate candidate generation from ranking and restrict optimization to retrieved candidates. Generative recommendation with Semantic IDs (SIDs) offers a path to end-to-end recommendation, but existing SID construction often lacks recommendation-aware semantics and effective local collaborative signals, while next-token prediction is misaligned with slate-level objectives. We propose OGR, an end-to-end framework that directly generates ordered slates-"Once Generated, Ranked." OGR first introduces TUSID, which adaptively fuses item-specific semantic and local collaborative information into hierarchical SIDs. It then uses list-wise preference planning and pipelined position-wise SID decoding to model global preferences and inter-item dependencies while generating ordered slates. We further propose SPA, a reward-guided conservative policy optimization method that aligns generated slates with user preferences beyond likelihood imitation. Offline experiments show that OGR outperforms representative baselines, with 48.2% and 27.2% relative NDCG@5 gains on industrial and public datasets, respectively. Online A/B testing on Kuaishou further yields a 1.120% improvement in Effective Views.

DrEM: Dual-Side Robust Ensemble Ranking from Noisy User Preference Predictions in Video Recommendation

Industrial video recommendation systems typically adopt a multi-stage architecture. At the ensemble ranking stage, multi-dimensional user preference predictions (pxtrs) from an upstream multi-task model are fused into a unified ranking score to reflect user satisfaction. Since users' true satisfaction is difficult to observe directly, ensemble ranking models commonly use pxtrs both as input features and as a source for constructing proxy preferences. However, as outputs of an upstream prediction model, pxtrs inevitably contain prediction noise, which propagates to downstream learning across two sides. On the supervision side, noisy pxtrs may flip proxy preferences and introduce erroneous gradients. On the feature side, pxtr noise may propagate through model inputs and destabilize ranking scores. Existing ensemble ranking methods typically treat pxtrs as reliable signals and overlook such prediction noise. To address this, we propose DrEM, a dual-side robust ensemble ranking framework. Our DrEM introduces a risk-denoising robust loss that corrects the empirical risk using estimated preference flip probability. Meanwhile, it samples perturbations from the distribution of prediction noise and introduces a preference-preserving ranking consistency regularizer to improve feature-side output stability. Theoretically, we obtain an approximate distribution of the prediction noise and prove that the robust loss remains superior under flip probability estimation error. Extensive offline experiments and large-scale online A/B tests demonstrate the effectiveness and robustness of our DrEM.

Uncertainty as Remedy: Mitigating Satisfaction Label Bias in Short Video Multi-Objective Ensemble Ranking

The core objective of short video recommendation is to model users' unobservable true satisfaction with recommended videos. As the dominant industrial framework, end-to-end multi-objective ensemble ranking models are typically trained with multi-dimensional dense user behavioral signals, such as clicks and watch time. However, these behavioral signals are partial, fragmented, and often mutually conflicting user satisfaction proxies, introducing uncertainty and label bias into satisfaction modeling. Conventional deterministic models overlook this uncertainty, which exacerbates satisfaction label bias and results in suboptimal model convergence. Meanwhile, existing uncertainty-aware methods mostly employ uncertainty for post-hoc ranking adjustments rather than leveraging it as a remedy to mitigate the inherent bias within the core optimization pipeline. This paper proposes UAME, an Uncertainty-Aware end-to-end Multi-objective Ensemble ranking framework for short video recommendation. UAME represents the model's prediction as a Gaussian scoring variable, where the mean denotes the predicted satisfaction score and the variance quantifies predictive uncertainty associated with this score. We further design a probabilistic pairwise ranking loss, and construct an uncertainty-aware sample-level weighting scheme to mitigate the bias. We further provide theoretical analysis suggesting that the weighting scheme helps mitigate satisfaction label bias. Extensive offline and online experiments on a large-scale industrial short video platform demonstrate that UAME consistently improves two state-of-the-art paradigms, EMER and EASQ, and better aligns with questionnaire-based user satisfaction. UAME has been deployed in our production short-video recommendation system and continues to deliver stable, statistically significant gains.

HGenPush: A Heterogeneous Generative Recommendation Architecture for Industrial Push Notification Systems

With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms as an example, users not only seek high-quality content but also trusted authors. Although generative recommendation systems have achieved breakthroughs in recent years, existing methods primarily generate single-type recommendation content and typically employ the inefficient autoregressive paradigm to generate semantic IDs. In this paper, we propose an end-to-end heterogeneous generative recommendation architecture called HGenPush. First, we design a hybrid user behavior understanding module that integrates multi-scenario and multi-perspective behaviors to capture precise user interest. Then, we design a dual-branch heterogeneous generative recommendation module that integrates video recommendation and author recommendation within a unified framework. In addition, to improve generation efficiency, we design a lightweight multi-token prediction method that discards the autoregressive paradigm. Finally, we design a user consumption preference alignment module, which leverages user feedback as reward signals to guide the model toward generating higher-quality content, thereby enhancing user experience and engagement. Through these designs, HGenPush simultaneously fulfills users' demands for high-quality content and trusted authors. We have deployed HGenPush on the push notification system of Kuaishou, a large-scale short-video platform, achieving a significant 0.181% increase in daily active users.

From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling

Modern online platforms increasingly adopt multi-page architectures to accommodate diverse user needs. On these platforms, page navigation (the process of directing users to specific functional pages upon app entry) serves as a critical gateway that shapes user's first impression and significantly influences subsequent engagement. To optimize this process, Kuaishou formulated the task of Personalized Landing Page Modeling (PLPM) and proposed KLAN, a reinforcement learning framework built upon Conservative Q-Learning (CQL). However, CQL-based approaches suffer from two fundamental limitations: (1) the Markov assumption fails to capture the strong non-Markovian temporal dependencies inherent in real-world user behaviors, and (2) TD learning with bootstrapping incurs severe cumulative errors and credit assignment difficulties under delayed rewards, particularly in long-horizon settings where users enter the app multiple times daily. To address these limitations, we propose GLAN (Generative Landing-page Adaptive Navigator), a sequence modeling framework built on Decision Transformer to tackle PLPM from a unified global-local perspective. Specifically, GLAN incorporates two key modules. First, we design the L-RTG module that captures users' inter-day consumption dynamics to provide accurate global guidance for all page assignments within a day. Furthermore, we propose the HRM module that decomposes session-level feedback into fine-grained signals, enabling precise local supervision for each page assignment. Extensive online experiments conducted on the Kuaishou platform demonstrate the effectiveness of GLAN, achieving +0.158\% and +0.108\% improvements on Daily Active Users (DAU) and user Lifetime (LT) respectively.

UniRank: Unified List-wise Reranking via Confidence-Ordered Denoising

List-wise reranking arranges a request-specific pool of candidate items into an ordered slate that maximizes user satisfaction. Existing generative rerankers fall into two paradigms: Autoregressive (AR) rerankers construct the slate left to right and capture inter-item dependencies in the exposure list, but they suffer from error propagation because early mistakes affect subsequent slots. Non-autoregressive (NAR) rerankers predict all slots in parallel and avoid error propagation, but they weaken inter-item interaction modeling under a slot independence assumption. This raises a central question: is there a unified architecture that combines the strengths of both paradigms and delivers stronger reranking performance? We answer this question with UniRank, a unified list-wise reranking framework whose inference time variants recover AR and NAR rerankers as special cases. UniRank integrates bidirectional slate modeling into an iterative denoising process and fills the most confident slot at each step. To instantiate this framework for reranking, we introduce the Task Grounded Diffusion Interface (TGD), which performs denoising at the item level and restricts prediction to the request-specific candidate pool. TGD aggregates each item's semantic tokens into a single item embedding and scores each slot directly against the candidate pool. Experiments on Amazon Books, MovieLens-1M, and an industrial short video dataset show that UniRank consistently outperforms state-of-the-art baselines. Online A/B tests on a real-world industrial platform further validate its effectiveness, yielding significant improvements of +0.159% in user average app-time and +1.016% in share-rate.

From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space

In modern recommender systems, list-wise reranking serves as a critical phase within the multi-stage pipeline, finalizing the exposed item sequence and directly impacting user satisfaction by modeling complex intra-list item dependencies. Existing methods typically formulate this task as selecting indices from the local input list. However, this approach suffers from a semantically inconsistent action space: the same output neuron (logits) represents different items across different samples, preventing the model from establishing a stable, intrinsic understanding of the items. To address this, we propose GloRank (Global Action Space Ranker), a generative framework that shifts reranking from selecting local indices to generating global identifiers. Specifically, we represent items as sequences of discrete tokens and reformulate reranking as a token generation task. This design effectively decouples the scoring mechanism from the variable input order, ensuring that items are evaluated against a consistent global standard. We further enhance this with a two-stage optimization pipeline: a supervised pre-training phase to initialize the model with high-quality demonstrations, followed by a reinforcement learning-based post-training phase to directly maximize list-wise utility. Extensive experiments on two public benchmarks and a large-scale industrial dataset, coupled with online A/B tests, demonstrate that GloRank consistently outperforms state-of-the-art baselines and achieves superior robustness in cold-start scenarios.

SOLAR: SVD-Optimized Lifelong Attention for Recommendation

Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its $O(N^2 d)$ time and memory cost in sequence length $N$ makes long-context modeling expensive and often forces truncation or other heuristics. Linear attention reduces complexity to $O(N d^2)$ by reordering computation through kernel feature maps, but this reformulation drops the softmax mechanism and shifts the attention score distribution. In recommender systems, low-rank structure in matrices is not a rare case, but rather the default inductive bias in its representation learning, particularly explicit in the user behavior sequence modeling. Leveraging this structure, we introduce SVD-Attention, which is theoretically lossless on low-rank matrices and preserves softmax while reducing attention complexity from $O(N^2 d)$ to $O(Ndr)$. With SVD-Attention, we propose SOLAR, SVD-Optimized Lifelong Attention for Recommendation, a sequence modeling framework that supports behavior sequences of ten-thousand scale and candidate sets of several thousand items in cascading process without any filtering. In Kuaishou's online recommendation scenario, SOLAR delivers a 0.68\% Video Views gain together with additional business metrics improvements.

FlashEvaluator: Expanding Search Space with Parallel Evaluation

The Generator-Evaluator (G-E) framework, i.e., evaluating K sequences from a generator and selecting the top-ranked one according to evaluator scores, is a foundational paradigm in tasks such as Recommender Systems (RecSys) and Natural Language Processing (NLP). Traditional evaluators process sequences independently, suffering from two major limitations: (1) lack of explicit cross-sequence comparison, leading to suboptimal accuracy; (2) poor parallelization with linear complexity of O(K), resulting in inefficient resource utilization and negative impact on both throughput and latency. To address these challenges, we propose FlashEvaluator, which enables cross-sequence token information sharing and processes all sequences in a single forward pass. This yields sublinear computational complexity that improves the system's efficiency and supports direct inter-sequence comparisons that improve selection accuracy. The paper also provides theoretical proofs and extensive experiments on recommendation and NLP tasks, demonstrating clear advantages over conventional methods. Notably, FlashEvaluator has been deployed in online recommender system of Kuaishou, delivering substantial and sustained revenue gains in practice.

Towards End-to-End Alignment of User Satisfaction via Questionnaire in Video Recommendation

Short-video recommender systems typically optimize ranking models using dense user behavioral signals, such as clicks and watch time. However, these signals are only indirect proxies of user satisfaction and often suffer from noise and bias. Recently, explicit satisfaction feedback collected through questionnaires has emerged as a high-quality direct alignment supervision, but is extremely sparse and easily overwhelmed by abundant behavioral data, making it difficult to incorporate into online recommendation models. To address these challenges, we propose a novel framework which is towards End-to-End Alignment of user Satisfaction via Questionaire, named EASQ, to enable real-time alignment of ranking models with true user satisfaction. Specifically, we first construct an independent parameter pathway for sparse questionnaire signals by combining a multi-task architecture and a lightweight LoRA module. The multi-task design separates sparse satisfaction supervision from dense behavioral signals, preventing the former from being overwhelmed. The LoRA module pre-inject these preferences in a parameter-isolated manner, ensuring stability in the backbone while optimizing user satisfaction. Furthermore, we employ a DPO-based optimization objective tailored for online learning, which aligns the main model outputs with sparse satisfaction signals in real time. This design enables end-to-end online learning, allowing the model to continuously adapt to new questionnaire feedback while maintaining the stability and effectiveness of the backbone. Extensive offline experiments and large-scale online A/B tests demonstrate that EASQ consistently improves user satisfaction metrics across multiple scenarios. EASQ has been successfully deployed in a production short-video recommendation system, delivering significant and stable business gains.