User profiling is pivotal for recommendation systems, as it transforms raw user interaction data into concise and structured representations that drive personalized recommendations. While traditional embedding-based profiles lack interpretability and adaptability, recent advances with large language models (LLMs) enable text-based profiles that are semantically richer and more transparent. However, existing methods often adhere to fixed formats that limit their ability to capture the full diversity of user behaviors. In this paper, we introduce LettinGo, a novel framework for generating diverse and adaptive user profiles. By leveraging the expressive power of LLMs and incorporating direct feedback from downstream recommendation tasks, our approach avoids the rigid constraints imposed by supervised fine-tuning (SFT). Instead, we employ Direct Preference Optimization (DPO) to align the profile generator with task-specific performance, ensuring that the profiles remain adaptive and effective. LettinGo operates in three stages: (1) exploring diverse user profiles via multiple LLMs(2) evaluating profile quality based on their impact in recommendation systems, and (3) aligning the profile generation through pairwise preference data derived from task performance. Experimental results demonstrate that our framework significantly enhances recommendation accuracy, flexibility, and contextual awareness. This work enhances profile generation as a key innovation for next-generation recommendation systems.

Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU learning is to sample a set of pseudo-negatives from the unlabeled data using ad-hoc thresholds so that conventional supervised methods can be applied with both positive and negative samples. Owing to the label uncertainty among the unlabeled data, errors of misclassifying unlabeled positive samples as negative samples inevitably appear and may even accumulate during the training processes. Those errors often lead to performance degradation and model instability. To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first. Similar intuition has been utilized in curriculum learning to only use easier cases in the early stage of training before introducing more complex cases. Specifically, we utilize a novel ''hardness'' measure to distinguish unlabeled samples with a high chance of being negative from unlabeled samples with large label noise. An iterative training strategy is then implemented to fine-tune the selection of negative samples during the training process in an iterative manner to include more ''easy'' samples in the early stage of training. Extensive experimental validations over a wide range of learning tasks show that this approach can effectively improve the accuracy and stability of learning with positive and unlabeled data. Our code is available at https://github.com/woriazzc/Robust-PU.

One of the most common threats to online service system's reliability is disk failure. Many disk failure prediction techniques have been developed to predict failures before they actually occur, allowing proactive steps to be taken to minimize service disruption and increase service reliability. Existing approaches for disk failure prediction do not differentiate among various types of disk failure. In industrial practice, however, different product teams treat distinct types of disk failures as different prediction tasks in large-scale online service systems like Microsoft 365. For example, hardware operation team is concerned with physical disk errors, while database service team focuses on I/O delay. In this paper, we propose MTHC (Multi-Task Hierarchical Classification) to enhance the performance of disk failure prediction for each task via multi-task learning. In addition, MTHC introduces a novel hierarchy-aware mechanism to deal with the data imbalance problem, which is a severe issue in the area of disk failure prediction. We show that MTHC can be easily utilized to enhance most state-of-the-art disk failure prediction models. Our experiments on both industrial and public datasets demonstrate that such disk failure prediction models enhanced by MTHC performs much better than those models working without MTHC. Furthermore, our experiments also present that the hierarchical-aware mechanism underlying MTHC can alleviate the data imbalance problem and thus improve the practical performance of various disk failure prediction models. More encouragingly, the proposed MTHC has been successfully applied to Microsoft 365 online service systems, and averagely reduces the number of virtual machine interruptions by 10% per month.

Large-scale distributed systems, such as Microsoft 365's database system, require timely mitigation solutions to address failures and improve service availability and reliability. Still, mitigation actions can be costly as they may cause temporal performance degradation and even incur monetary expenses. Mitigation actions can be either administrated in a reactive fashion to contain detected failures or a proactive fashion to reduce potential failures. The proactive mitigation approach typically relies on a two-stage strategy: the prediction model will firstly identify instances (such as databases or disks) with high failure risk, then appropriate mitigation actions chosen by engineers or an automatic bandit learning model can be applied. As information is not fully shared across those two stages, important factors such as mitigation costs and states of instances are often ignored in one of those two stages. To address these issues, we propose NENYA, an end-to-end mitigation solution for a large-scale database system powered by a novel cascade reinforcement learning model. By taking the states of databases as input, NENYA directly outputs mitigation actions and is optimized based on jointly cumulative feedback on mitigation costs and failure rates. As the overwhelming majority of databases do not require mitigation actions, NENYA utilizes a novel cascade decision structure to firstly reliably filter out such databases and then focus on choosing appropriate mitigation actions for the rest. Extensive offline and online experiments have shown that our methods can outperform existing practices in reducing both failure rates of databases and mitigation costs. NENYA has been integrated into Microsoft 365, a productive platform, with sounding success.

Click-through rate (CTR) prediction aims to recall the advertisements that users are interested in and to lead users to click, which is of critical importance for a variety of online advertising systems. In practice, CTR prediction is generally formulated as a conventional binary classification problem, where the clicked advertisements are positive samples and the others are negative samples. However, directly treating unclicked advertisements as negative samples would suffer from the severe label noise issue, since there exist many reasons why users are interested in a few advertisements but do not click. To address such serious issue, we propose a reinforcement learning based noise filtering approach, dubbed RLNF, which employs a noise filter to select effective negative samples. In RLNF, such selected, effective negative samples can be used to enhance the CTR prediction model, and meanwhile the effectiveness of the noise filter can be enhanced through reinforcement learning using the performance of CTR prediction model as reward. Actually, by alternating the enhancements of the noise filter and the CTR prediction model, the performance of both the noise filter and the CTR prediction model is improved. In our experiments, we equip 7 state-of-the-art CTR prediction models with RLNF. Extensive experiments on a public dataset and an industrial dataset present that RLNF significantly improves the performance of all these 7 CTR prediction models, which indicates both the effectiveness and the generality of RLNF.

With the rapid deployment of cloud platforms, high service reliability is of critical importance. An industrial cloud platform contains a huge number of disks, and disk failure is a common cause of service unreliability. In recent years, many machine learning based disk failure prediction approaches have been proposed, and they can predict disk failures based on disk status data before the failures actually happen. In this way, proactive actions can be taken in advance to improve service reliability. However, existing approaches treat each disk individually and do not explore the influence of the neighboring disks. In this paper, we propose Neighborhood-Temporal Attention Model (NTAM), a novel deep learning based approach to disk failure prediction. When predicting whether or not a disk will fail in near future, NTAM is a novel approach that not only utilizes a disk’s own status data, but also considers its neighbors’ status data. Moreover, NTAM includes a novel attention-based temporal component to capture the temporal nature of the disk status data. Besides, we propose a data enhancement method, called Temporal Progressive Sampling (TPS), to handle the extreme data imbalance issue. We evaluate NTAM on a public dataset as well as two industrial datasets collected from millions of disks in Microsoft Azure. Our experimental results show that NTAM significantly outperforms state-of-the-art competitors. Also, our empirical evaluations indicate the effectiveness of the neighborhood-ware component and the temporal component underlying NTAM as well as the effectiveness of TPS. More encouragingly, we have successfully applied NTAM and TPS to Microsoft cloud platforms (including Microsoft Azure and Microsoft 365) and obtained benefits in industrial practice.