With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms (e.g., short video platforms). However, short videos often contain diverse segments, and users may not hold the same attitude toward all of them (e.g., music enthusiasts may not enjoy all songs in a medley). Traditional binary-classification recommendation models, which treat a video as a single holistic entity, face limitations in accurately capturing such nuanced preferences. Considering that user consumption is a temporal process, this paper demonstrates that the timing of user actions can represent diverse intentions through statistical analysis and examination of action patterns. Based on this insight, we propose a novel modeling paradigm: Action-Aware Gen erative Sequence Network (A2Gen ), which refines user actions (e.g., Like and Follow, etc.) along the temporal dimension and chains them into sequences for unified processing and prediction. First, we introduce the Context-aware Attention Module (CAM) to model action sequences enriched with item-specific contextual features. Building upon this, we develop the Hierarchical Sequence Encoder (HSE) to learn temporal action patterns from users' historical actions. Finally, through leveraging CAM, we design a module for action sequence generation: the Action-seq Autoregressive Generator (AAG). Extensive offline experiments on the Kuaishou's dataset and the Tmall public dataset demonstrate the superiority of our proposed model. Furthermore, through large-scale online A/B testing deployed on Kuaishou's platform, our model achieves significant improvements over baseline methods in multi-task prediction by leveraging sequential information. Specifically, it yields increases of 0.34% in user watch time, 8.1% in interaction rate, and 0.162% in overall user retention (LifeTime-7), leading to successful deployment across all traffic, serving over 400 million users every day.

Real-world networks, particularly those in web and social media, are dynamic with evolving node attributes and structures, often involving billions of nodes and edges. Dynamic attributed network embedding is a powerful tool for capturing these changes, enabling data owners and problem owners to better understand interactions and trends for more effective engagement and decision-making. While some existing algorithms are capable of handling very large-scale dynamic attributed networks with billions of nodes and edges, they often suffer from accuracy loss or high computational overhead. In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. By the sparse random projection technique in an incremental update manner, VLS2ketch significantly reduces the energy-intensive computational workload while maintaining accuracy. Also, we introduce an information decay mechanism, which adapts to temporally varying topologies and node attributes. This mechanism ensures that outdated information gradually diminishes over time. Extensive experiments on real-world very large-scale datasets demonstrate that our proposed VLS2ketch method delivers comparable embedding quality against the state-of-the-art learning-based competitors with dramatically reduced runtime. We have released the source code and the datasets in https://github.com/AIandBD/graph-hashing/tree/main/VLS2ketch. .

Attributed network embedding seeks to depict each network node via a compact, low-dimensional vector while effectively preserving the similarity between node pairs, which lays a strong foundation for a great many high-level network mining tasks. With the advent of the era of Big Data, the number of nodes and edges has reached billions in many real-world networks, which poses great computational and storage challenges to the existing methods. Although some algorithms have been developed to handle billion-scale networks, they often undergo accuracy degradation or tempo-spatial inefficiency owing to attribute information loss or substantial parameter learning. To this end, we propose a simple, time- and space-efficient billion-scale attributed network embedding algorithm called SketchBANE in this paper, which strikes an excellent balance between accuracy and efficiency by adopting sparse random projection with 1-bit quantization to sketch the iterative closed neighborhood and maintain the similarity among high-order nodes in a non-learning manner. The extensive experimental results indicate that our proposed SketchBANE algorithm competes favorably with the state-of-the-art approaches, while remarkably reducing runtime and space consumption. Also, the proposed SketchBANE algorithm exhibits good scalability and parallelization.

In the realm of modern mobile E-commerce, providing users with nearby commercial service recommendations through location-based online services has become increasingly vital. While machine learning approaches have shown promise in multi-scene recommendation, existing methodologies often struggle to address cold-start problems in unprecedented scenes: the increasing diversity of commercial choices, along with the short online lifespan of scenes, give rise to the complexity of effective recommendations in online and dynamic scenes. In this work, we propose Scene-wise Adaptive Network (SwAN 1), a novel approach that emphasizes high-performance cold-start online recommendations for new scenes. Our approach introduces several crucial capabilities, including scene similarity learning, user-specific scene transition cognition, scene-specific information construction for the new scene, and enhancing the diverged logical information between scenes. We demonstrate SwAN’s potential to optimize dynamic multi-scene recommendation problems by effectively online handling cold-start recommendations for any newly arrived scenes. More encouragingly, SwAN has been successfully deployed in Meituan’s online catering recommendation service, which serves millions of customers per day, and SwAN has achieved a 5.64% CTR index improvement relative to the baselines and a 5.19% increase in daily order volume proportion.

Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture. However, the MoE-based method, which aims to project all information in the same feature space, cannot effectively deal with the complex relationships inherent among various scenarios and tasks, resulting in unsatisfactory performance. To tackle the problem, we propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation, which achieves hierarchical extraction based on coarse-to-fine knowledge transfer scheme. The multiple extraction layers of the hierarchical network enable the model to enhance the capability of transferring valuable information across scenarios while preserving specific features of scenarios and tasks. Furthermore, a novel scenario-aware attentive network module is proposed to model correlations between scenarios explicitly. Comprehensive experiments conducted on real-world industrial datasets from Meituan Meishi platform demonstrate that HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions. HiNet is currently fully deployed in two scenarios and has achieved 2.87% and 1.75% order quantity gain respectively.

Graphs, as a non-linear data structure, are ubiquitous in practice, and efficient graph analysis can benefit important information retrieval applications in the era of big data. Currently, one of the fundamental graph mining problems is graph embedding, which aims to represent the graph as a low-dimensional feature vector with the content and structural information in the graph preserved. Although the graph embedding technique has evolved considerably, traditional methods mainly focus on node pairwise relationship in graphs, which makes the representational power of such schemes limited. Recently, a number of works have explored the simplicial complexes, which describe the higher-order interactions between nodes in the graphs, and further proposed several Graph Neural Network (GNN) algorithms based on simplicial complexes. However, these GNN approaches are highly inefficient in terms of running time and space, due to massive parameter learning. In this paper, we propose a simple and speedy graph embedding algorithm dubbed SCHash. Through adopting the Locality Sensitive Hashing (LSH) technique, SCHash captures the higher-order information derived from the simplicial complex in the GNN framework, and it can achieve a good balance between accuracy and efficiency. Our extensive experiments clearly show that, in terms of accuracy, the performance of our proposed SCHash algorithm is comparable to that of state-of-the-art GNN algorithms; also, SCHash achieves higher accuracy than the existing LSH algorithms. In terms of efficiency, SCHash runs faster than GNN algorithms by 2 ~ 4 orders of magnitude, and is more efficient than the existing LSH algorithms.

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.

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.

Feature engineering is a crucial step for developing effective machine learning models. Traditionally, feature engineering is performed manually, which requires much domain knowledge and is time-consuming. In recent years, many automated feature engineering methods have been proposed. These methods improve the accuracy of a machine learning model by automatically transforming the original features into a set of new features. However, existing methods either lack ability to perform high-order transformations or suffer from the feature space explosion problem. In this paper, we present Neural Feature Search (NFS), a novel neural architecture for automated feature engineering. We utilize a recurrent neural network based controller to transform each raw feature through a series of transformation functions. The controller is trained through reinforcement learning to maximize the expected performance of the machine learning algorithm. Extensive experiments on public datasets illustrate that our neural architecture is effective and outperforms the existing state-of-the-art automated feature engineering methods. Our architecture can efficiently capture potentially valuable high-order transformations and mitigate the feature explosion problem.