Cloud Workload Prediction (CWP) is a critical task in cloud computing, essential for resource scheduling, performance optimization, and cost management. However, existing time series prediction methods struggle with instability and inefficiency when applied directly to cloud workloads due to their high variability and frequent fluctuations. To address these challenges, we propose DEL4CW, a novel D eep E xpansion L earning framework specifically designed for CWP . DEL4CW introduces a unique self-decoupling mechanism to disentangle the complex dependencies present in highly variable cloud workloads, leading to more accurate predictions of job arrival rates. The core contribution of DEL4CW lies in its ability to decouple cloud workload signals into three key components—trend, periodicity, and residuals—by treating these as hidden variables. This enables the model to better manage both short-term fluctuations and long-term workload trends. DEL4CW employs a deep expansion learning framework structured as stacked blocks, where each block includes dedicated modules for trend, periodicity, and compensation. Specifically, the trend module utilizes multi-layer fully connected networks to capture evolving trends at multiple granularities, while the periodicity module leverages multi-head attention to identify diverse periodic patterns. The compensation module addresses unpredictable, localized fluctuations, improving the model’s robustness to noise. In addition to its predictive accuracy, DEL4CW provides interpretable insights through its hierarchical design, allowing for layer-by-layer aggregation of meaningful partial predictions. This interpretability stems from the doubly residual learning pipeline, which ensures that each prediction block contributes progressively refined predictions. Extensive experiments on real-world cloud workload traces demonstrate that DEL4CW significantly outperforms existing baselines, with error reductions reaching up to 27.74% in certain scenarios.

As recommender systems are essential to various web domains such as e-commerce and web content sharing, providing equitable item exposure regardless of popularity becomes an imperative requirement. However, traditional fairness-aware approaches typically aim to achieve a better tradeoff between recommendation accuracy and fairness, and focus on improving the exposure rate of the long-tail items on static settings, evaluating fairness on one-shot recommendation decisions using logged data. Such methods overlook the dynamic nature of user preferences in real-world interactive environments. In contrast, our work seeks a win-win solution that simultaneously enhances recommendation accuracy and fairness over the long term, rather than merely trading off one against the other. To achieve this goal, we empirically demonstrate and analyze the spatiotemporal heterogeneity of user popularity preference. Our findings reveal complementary characteristics that, when fully exploited, can guide personalized strategies for long-term fairness. Building on this insight, we propose HER4IF, a novel hierarchical reinforcement learning framework designed for interactive recommendation. HER4IF decomposes the recommendation process into two key tasks: dynamic fairness control and item recommendation. The high-level agent continuously learns adaptive fairness constraints from evolving user popularity preferences, while the low-level agent refines recommendation policies under these personalized constraints. Extensive experiments on three real-world datasets and the interactive recommendation platform KuaiSim demonstrate that HER4IF significantly outperforms state-of-the-art methods, achieving substantial improvements in both fairness and recommendation accuracy. Our code is available at: https://github.com/1163710212/HER4IF .

It is a longstanding problem that Q-learning suffers from the overestimation bias. This issue originates from the fact that Q-learning uses the expectation of maximum Q-value to approximate the maximum expected Q-value. A number of algorithms, such as Double Q-learning, were proposed to address this problem by reducing the estimation of maximum Q-value, but this may lead to an underestimation bias. Note that this underestimation bias may have a larger performance penalty than the overestimation bias. Different from previous algorithms, this article studies this issue from a fresh perspective, i.e., meta-learning view, which leads to our Meta-Debias Q-learning. The main idea is to extract the maximum expected Q-value with meta-learning over multiple tasks to remove the estimation bias of maximum Q-value and help the agent choose the optimal action more accurately. However, there are two challenges: (1) How to automatically select suitable training tasks? (2) How to positively transfer the meta-knowledge from selected tasks to remove the estimation bias of maximum Q-value? To address the two challenges mentioned above, we quantify the similarity between the training tasks and the test task. This similarity enables us to select appropriate “partial” training tasks and helps the agent extract the maximum expected Q-value to remove the estimation bias. Extensive experiment results show that our Meta-Debias Q-learning outperforms SOTA baselines drastically in three evaluation indicators, i.e., maximum Q-value, policy, and reward. More specifically, our Meta-Debias Q-learning only underestimates \(1.2*10^{-3}\) than the maximum expected Q-value in the multi-armed bandit environment and only differs \(5.04\%-5\%=0.04\%\) than the optimal policy in the two states MDP environment. In addition, we compare the uniform weight and our similarity weight. Experiment results reveal fundamental insights into why our proposed algorithm outperforms in the maximum Q-value, policy, and reward.

Online rating system serves as an indispensable building block for many web applications. Previous studies showed that due to assimilate-contrast effects, historical ratings could significantly distort users' ratings, leading to low accuracy of product quality estimation and recommendation. To understand assimilate-contrast effects, an “accurate” model is still missing as previous models do not capture important factors like rating recency, selection bias, etc. Furthermore, an analytical framework to characterize product estimation accuracy under assimilate-contrast effects is also missing. This paper aims to fill in this gap. We propose a probabilistic model to quantify the aforementioned important factors on assimilate-contrast effects. We apply stochastic approximation theory to show that when the rating bias satisfies mild contraction conditions, the aggregate rating converges under aggregate opinion heterogeneity. We also apply non-stationary Markov chain theory to show that when the strength of assimilate-contrast satisfies mild stable conditions, the aggregate rating converges under rating recency. We also derive an equation to characterize the converged aggregate ratings. These conditions reveal important insights on how the aforementioned factors influence the convergence and guide the online rating system operator to design appropriate rating aggregation rules and rating displaying strategies. We apply it to rating prediction tasks and product recommendation tasks. Experiment results on four public datasets show that our model can improve the rating prediction and recommendation accuracy over previous models significantly, under various metrics like RMSE, NDCG, etc. We also demonstrate the flexibility of our model by showing that it can be applied to enhance other rating behavior models.

While personalization increases the utility of item recommendation, it also suffers from the issue of popularity bias. However, previous methods emphasize adopting supervised learning models to relieve popularity bias in the static recommendation, ignoring the dynamic transfer of user preference and amplification effects of the feedback loop in the recommender system (RS). In this paper, we focus on studying this issue in the interactive recommendation. We argue that diversification and novelty are both equally crucial for improving user satisfaction of IRS in the aforementioned setting. To achieve this goal, we propose a D iversity- N ovelty- a ware I nteractive R ecommendation framework (DNaIR) that augments offline reinforcement learning (RL) to increase the exposure rate of long-tail items with high quality. Its main idea is first to aggregate the item similarity, popularity, and quality into the reward model to help the planning of RL policy. It then designs a diversity-aware stochastic action generator to achieve an efficient and lightweight DNaIR algorithm. Extensive experiments are conducted on the three real-world datasets and an authentic RL environment (Virtual-Taobao). The experiments show that our model can better and full use of the long-tail items to improve recommendation satisfaction, especially those low popularity items with high-quality ones, thus achieving state-of-the-art performance.