Modern internet platforms usually have different scenarios to provide rich recommendation services to meet the diverse demands of users. Cross-domain recommendation (CDR) and multi-domain recommendation (MDR) methods are widely used in such platforms to leverage rich auxiliary information from multiple domains. However, state-of-the-art CDR and MDR methods usually enforce some correlations between source and target embeddings on each user, ignoring the correlations between users in both domains. To address this problem, we adopt a relaxed contrastive loss, that employs the pairwise similarities in the source domain as relaxed labels, enforcing such inter-sample relations are reserved in a weighted manner in the target domain. The basic assumption behind such a design is that users with similar interests should be with similar interacted items in a rec- ommender system, and this work takes a step further to realize and specify such similarity modeling as collaborative signals encoded in both implicit embedding spaces. We validate the effectiveness of the proposed method on a large- scale public dataset and a real production dataset with over 700 million samples. We further experimentally show that the proposed embedding transfer method is generic, and can be plugged into any existing deep neural networks, such as YoutubeDNN and BERT4Rec. Currently, the proposed embedding transfer techniques have been successfully deployed in the Guess You Like in WeTV for the CDR/MDR task.

In this study, sentiment classification and emotion distribution learning across domains are both formulated as a semi-supervised domain adaptation problem, which utilizes a small amount of labeled documents in the target domain for model training. By introducing a shared matrix that captures the stable association between document clusters and word clusters, non-negative matrix tri-factorization (NMTF) is robust to the labeled target domain data and has shown remarkable performance in cross-domain text classification. However, the existing NMTF-based models ignore the incompatible relationship of sentiment polarities and the relatedness among emotions. Besides, their applications on large-scale datasets are limited by the high computation complexity. To address these issues, we propose a semi-supervised NMTF framework for sentiment classification and emotion distribution learning across domains. Based on a many-to-many mapping between document clusters and sentiment polarities (or emotions), we first incorporate the prior information of label dependency to improve the model performance. Then, we develop a parallel algorithm based on message passing interface (MPI) to further enhance the model scalability. Extensive experiments on real-world datasets validate the effectiveness of our method.

As a novel paradigm for data mining and dimensionality reduction, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention due to its notable performance and elegant mathematical derivation, and it has been applied to a plethora of real-world applications, such as text data co-clustering. However, the existing NMTF-based methods usually involve intensive matrix multiplications, which exhibits a major limitation of high computational complexity. With the explosion at both the size and the feature dimension of texts, there is a growing need to develop a parallel and scalable NMTF-based algorithm for text data co-clustering. To this end, we first show in this paper how to theoretically derive the original optimization problem of NMTF by introducing the Lagrangian multipliers. Then, we propose to solve the Lagrange dual objective function in parallel through an efficient distributed implementation. Extensive experiments on five benchmark corpora validate the effectiveness, efficiency, and scalability of our distributed parallel update algorithm for an NMTF-based text data co-clustering method.

Software architecture is undergoing a transition from monolithic architectures to microservices to achieve resilience, agility, and scalability in the software life circle. However, microservice architecture is not perfect and suffers from intermittent faults, leading to economic and user losses. Therefore, it is essential to detect anomalies in microservice systems accurately. The key limitation of current approaches lies in a lack of ability to detect multitype anomalies, excessive resource overhead, and requirements of expert knowledge. In this paper, we present a Deep Attentive anomaly detection approach with Multimodal data named DAM. With multimodal fusion, attentive LSTM, and a dynamic threshold selecting algorithm, DAM could detect anomalies accurately and efficiently in an unsupervised manner. We evaluate our approach by injecting six types of anomalies on a widely used microservice system, Train-Ticket. The result shows that DAM could detect multitype anomalies well, with 80.46% F-measure, achieving 16.76% and 29.52% improvement over two state-of-the-art baselines (Donut and DAGMM), respectively.