Short-video recommendation systems typically optimize for user satisfaction. However, allocating exposure to creators at critical growth stages incentivizes long-term content supply despite compromising immediate user engagement. Existing efforts concerning creator interests aim at either improving creator exposure fairness, matching creators with suitable audiences, or leveraging creator behavior to enhance user welfare. Directly maximizing the joint value of user satisfaction and creator incentive at recommendation time, however, remains largely unaddressed. This presents two challenges. First, the two objectives are heterogeneous in nature, making it non-trivial to formulate this joint optimization as a tractable problem. Second, optimizing creator incentive requires globally coordinated decisions across requests, making real-time serving infeasible. To address these challenges, we formulate the joint maximization as a constrained optimization problem that unifies the two heterogeneous objectives. We further derive an efficient online algorithm based on the primal-dual method, which decouples global incentive constraints into real-time decisions with theoretical guarantees. Experiments on a large-scale short-video platform demonstrate consistent improvements in joint user-creator value over existing baselines.

Ranking models primarily focus on modeling the relative order of predictions while often neglecting the significance of the accuracy of their absolute values. However, accurate absolute values are essential for certain downstream tasks, necessitating the calibration of the original predictions. To address this, existing calibration approaches typically employ predefined transformation functions with order-preserving properties to adjust the original predictions. Unfortunately, these functions often adhere to fixed forms, such as piece-wise linear functions, which exhibit limited expressiveness and flexibility, thereby constraining their effectiveness in complex calibration scenarios. To mitigate this issue, we propose implementing a calibrator using an Unconstrained Monotonic Neural Network (UMNN), which can learn arbitrary monotonic functions with great modeling power. This approach significantly relaxes the constraints on the calibrator, improving its flexibility and expressiveness while avoiding excessively distorting the original predictions by requiring monotonicity. Furthermore, to optimize this highly flexible network for calibration, we introduce a novel additional loss function termed Smooth Calibration Loss (SCLoss), which aims to fulfill a necessary condition for achieving the ideal calibration state. Extensive offline experiments confirm the effectiveness of our method in achieving superior calibration performance. Moreover, deployment in Kuaishou's large-scale online video ranking system demonstrates that the method's calibration improvements translate into enhanced business metrics. The source code is available at https://github.com/baiyimeng/UMC.

Scale-calibrated ranking systems are ubiquitous in real-world applications nowadays, which pursue accurate ranking quality and calibrated probabilistic predictions simultaneously.For instance, in the advertising ranking system, the predicted click-through rate (CTR) is utilized for ranking and required to be calibrated for the downstream cost-per-click ads bidding.Recently, multi-objective based methods have been wildly adopted as a standard approach for Calibrated Ranking, which incorporates the combination of two loss functions: a pointwise loss that focuses on calibrated absolute values and a ranking loss that emphasizes relative orderings.However, when applied to industrial online applications, existing multi-objective CR approaches still suffer from two crucial limitations.First, previous methods need to aggregate the full candidate list within a single mini-batch to compute the ranking loss.Such aggregation strategy violates extensive data shuffling which has long been proven beneficial for preventing overfitting, and thus degrades the training effectiveness.Second, existing multi-objective methods apply the two inherently conflicting loss functions on a single probabilistic prediction, which results in a sub-optimal trade-off between calibration and ranking.To tackle the two limitations, we propose a Self-Boosted framework for Calibrated Ranking (SBCR).In SBCR, the predicted ranking scores by the online deployed model are dumped into context features.With these additional context features, each single item can perceive the overall distribution of scores in the whole ranking list, so that the ranking loss can be constructed without the need for sample aggregation.As the deployed model is a few versions older than the training model, the dumped predictions reveal what was failed to learn and keep boosting the model to correct previously mis-predicted items.Moreover, a calibration module is introduced to decouple the point loss and ranking loss.The two losses are applied before and after the calibration module separately, which

This work develops a continuous sign language (SL) recognition framework with deep neural networks, which directly transcribes videos of SL sentences to sequences of ordered gloss labels. Previous methods dealing with continuous SL recognition usually employ hidden Markov models with limited capacity to capture the temporal information. In contrast, our proposed architecture adopts deep convolutional neural networks with stacked temporal fusion layers as the feature extraction module, and bidirectional recurrent neural networks as the sequence learning module. We propose an iterative optimization process for our architecture to fully exploit the representation capability of deep neural networks with limited data. We first train the end-to-end recognition model for alignment proposal, and then use the alignment proposal as strong supervisory information to directly tune the feature extraction module. This training process can run iteratively to achieve improvements on the recognition performance. We further contribute by exploring the multimodal fusion of RGB images and optical flow in sign language. Our method is evaluated on two challenging SL recognition benchmarks, and outperforms the state of the art by a relative improvement of more than 15% on both databases.

Connectionist Temporal Classification (CTC) is an objective function for end-to-end sequence learning, which adopts dynamic programming algorithms to directly learn the mapping between sequences. CTC has shown promising results in many sequence learning applications including speech recognition and scene text recognition. However, CTC tends to produce highly peaky and overconfident distributions, which is a symptom of overfitting. To remedy this, we propose a regularization method based on maximum conditional entropy which penalizes peaky distributions and encourages exploration. We also introduce an entropy-based pruning method to dramatically reduce the number of CTC feasible paths by ruling out unreasonable alignments. Experiments on scene text recognition show that our proposed methods consistently improve over the CTC baseline without the need to adjust training settings. Code has been made publicly available at: https://github.com/liuhu-bigeye/enctc.crnn.

This work presents a weakly supervised framework with deep neural networks for vision-based continuous sign language recognition, where the ordered gloss labels but no exact temporal locations are available with the video of sign sentence, and the amount of labeled sentences for training is limited. Our approach addresses the mapping of video segments to glosses by introducing recurrent convolutional neural network for spatio-temporal feature extraction and sequence learning. We design a three-stage optimization process for our architecture. First, we develop an end-to-end sequence learning scheme and employ connectionist temporal classification (CTC) as the objective function for alignment proposal. Second, we take the alignment proposal as stronger supervision to tune our feature extractor. Finally, we optimize the sequence learning model with the improved feature representations, and design a weakly supervised detection network for regularization. We apply the proposed approach to a real-world continuous sign language recognition benchmark, and our method, with no extra supervision, achieves results comparable to the state-of-the-art.