With the rapid growth of multimedia applications and content, multimodal recommendation systems have garnered significant attention due to their ability to leverage diverse data types for personalized recommendations. Existing methods, which primarily focus on extracting common features across modalities, encounter two critical limitations: (1) they often overlook modality-unique features that carry distinct and valuable information, and (2) they fail to effectively capture cooperative interactions between modalities, which are essential for comprehensive understanding. To address these challenges, we propose the Modality Decomposition Network for Multimodal Recommendation (MDN). MDN introduces a novel Multimedia Knowledge Decomposition module that systematically separates modality representations into three key components: common features, unique features, and cooperative features. This decomposition enables our model to learn richer and more comprehensive representations by explicitly modeling the interplay between shared and modality-unique information. Additionally, MDN incorporates a Multimodal Information Encoder to enhance item feature representation by integrating diverse data sources. Furthermore, a Multimodal Contrastive Enhancement Layer is designed to refine user and item representations through contrastive learning, ensuring robust and discriminative recommendations. Extensive experiments conducted on benchmark datasets demonstrate that MDN consistently outperforms existing state-of-the-art methods, achieving superior performance.

In click-through rate prediction, click-through rate prediction is used to model users' interests. However, most of the existing CTR prediction methods are mainly based on the ID modality. As a result, they are unable to comprehensively model users' multi-modal preferences. Therefore, it is necessary to introduce multi-modal CTR prediction. Although it seems appealing to directly apply the existing multi-modal fusion methods to click-through rate prediction models, these methods (1) fail to effectively disentangle commonalities and specificities across different modalities; (2) fail to consider the synergistic effects between modalities and model the complex interactions between modalities.

The sequential recommendation system utilizes historical user interactions to predict preferences. Effectively integrating diverse user behavior patterns with rich multimodal information of items to enhance the accuracy of sequential recommendations is an emerging and challenging research direction. This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences across different behaviors, as user attention to different item modalities varies depending on the behavior; (2) the difficulty of effectively mitigating implicit noise in user behavior, such as unintended actions like accidental clicks; (3) the inability to handle modality noise in multi-modal representations, which further impacts the accurate modeling of user preferences. To tackle these issues, we propose a novel Multi-Modal Multi-Behavior Sequential Recommendation model (M3BSR). This model first removes noise in multi-modal representations using a Conditional Diffusion Modality Denoising Layer. Subsequently, it utilizes deep behavioral information to guide the denoising of shallow behavioral data, thereby alleviating the impact of noise in implicit feedback through Conditional Diffusion Behavior Denoising. Finally, by introducing a Multi-Expert Interest Extraction Layer, M3BSR explicitly models the common and specific interests across behaviors and modalities to enhance recommendation performance. Experimental results indicate that M3BSR significantly outperforms existing state-of-the-art methods on benchmark datasets.

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.