Recommendation systems (RS) aim to provide personalized content, but they face a challenge in unbiased learning due to selection bias, where users only interact with items they prefer. This bias leads to a distorted representation of user preferences, which hinders the accuracy and fairness of recommendations. To address the issue, various methods such as error imputation based, inverse propensity scoring, and doubly robust techniques have been developed. Despite the progress, from the structural causal model perspective, previous debiasing methods in RS assume the independence of the exogenous variables. In this paper, we release this assumption and propose a learning algorithm based on likelihood maximization to learn a prediction model. We first discuss the correlation and difference between unmeasured confounding and our scenario, then we propose a unified method that effectively handles latent exogenous variables. Specifically, our method models the data generation process with latent exogenous variables under mild normality assumptions. We then develop a Monte Carlo algorithm to numerically estimate the likelihood function. Extensive experiments on synthetic datasets and three real-world datasets demonstrate the effectiveness of our proposed method. The code is at https://github.com/WallaceSUI/kdd25-background-variable.

The domain of time series forecasting has gained significant attention due to its critical applications in multimedia-rich web traffic (including video streaming workloads and dynamic content delivery) and cross-platform advertisement click predictions, which are essential for web operations planning. While models like TimeSieve have demonstrated strong capabilities in predicting web visitation metrics, they suffer from critical unfaithfulness issues, including sensitivity to random seeds, input noise, layer noise, and parametric perturbations. To address these limitations, we propose Faithful TimeSieve (FTS), an enhanced framework designed to improve prediction reliability and robustness. Our approach systematically detects and mitigates unfaithfulness in TimeSieve, significantly enhancing its stability and consistency. Experimental results demonstrate that FTS substantially improves the model's faithfulness, setting a new standard for temporal forecasting methods. This advancement not only increases TimeSieve's reliability but also contributes to more robust temporal modeling, particularly crucial for web traffic forecasting where prediction accuracy directly impacts operational decisions. Our work thus represents a significant step toward more dependable time series predictions in web-related applications.

Online video recommendation systems often build binary labels based on play complete rate (i.e., the ratio of watch time to video duration), such as complete play and effective play, using them as implicit feedback for Click-Through Rate (CTR) prediction tasks to gauge user interest. Existing works tend to improve prediction accuracy by designing complex models, overlooking that a key cause of inaccurate predictions is the disorganization of instance representation space. To address this issue, we explore a novel approach using prototype learning to calibrate the instance representation space of deep recommendation models and propose a model-agnostic Contrastive Prototype Framework (CPF). Firstly, CPF partitions the instance space into different subspaces based on duration, then generates positive and negative prototype pairs for each subspace from pre-trained recommendation model. Subsequently, we map the instance representations to the prototype space and calibrate them by reducing the distance to the corresponding prototypes. Ultimately, the prediction is derived from the linear combination of the estimated values associated with each prototype. To prevent disorganization in the prototype space during training, we design contrastive and orthogonality losses to constrain the learning of prototypes. Additionally, we show that how CPF effectively addresses the duration bias from the perspective of causal intervention. Offline experiments on two datasets demonstrate that CPF improves recommendation accuracy over several baseline models in predicting five widely used implicit feedback labels. We have also deployed CPF on a short video platform, validating its effectiveness in real-world scenarios.

Multi-domain and multi-task learning enhance the efficiency and performance of industrial recommendation systems by integrating information from different domains/tasks to model user interests uniformly. However, existing methods suffer from the problem of representation entanglement, which limits the effective handling of commonality and specificity among various domains/tasks. In this paper, we propose a Prototype-guided Representation Projection (PRP) model to address this issue, which explores a novel direction of applying prototype learning to deal with complex domain/task relationships in the recommendation field. To identify inter-domain/task commonality, PRP initially uses a shared Mixture of Experts (MoE) architecture to learn representations for each sample, projecting them into a common prototype space across all domains/tasks. For domain/task specificity, specific feature extraction experts are employed, and sample representations are projected to the corresponding prototype spaces, constrained by an orthogonal loss to ensure the independence of those spaces. Moreover, PRP utilizes Optimal Transport (OT) to guide the correct representation projection within the prototype spaces, employing the linear combination of prototypes as the new sample representation. We conduct offline experiments on two open-source datasets and deploy our approach in an online system for A/B testing. Extensive experimental results consistently demonstrate that our approach outperforms existing methods.