Sequential recommendation aims to derive insights from user interaction records and make predictions based on relationships between users and items. However, most existing approaches do not effectively integrate user intents and preferences, which limits their capability to capture user behavior patterns. Additionally, these methods often struggle with poor performance in sparse data scenarios. To address these challenges, we proposeEL4SR, a sequential recommendation approach by integrating intent understanding and preference learning.EL4SRsimultaneously learns user intents and preferences through a dual-channel recommendation module, modeling both item and popularity sequences to enable mutual learning that captures the combined effects of intent and preference. Moreover, we enhance intent learning through contrastive learning, improving adaptability in sparse data contexts. We design several augmentation operators to improve the performance and robustness ofEL4SR. Extensive experiments on MovieLens and Amazon demonstrate the performance of our proposed method across various scenarios.

The rapid development of the internet has led to an exponential increase in the scale of computing, storage, networking, and service resources. Traditional monolithic architectures are increasingly insufficient for managing these complexities. In contrast, microservice architectures have emerged as the mainstream solution with their inherent flexibility in deployment and scalability. To ensure system reliability, modern microservice architectures rely heavily on observability data, including logs, metrics, and traces. However, challenges such as network instability, service instance restarts, and system overloads frequently lead to intermittent loss of metric data. These missing data points impede comprehensive assessments of system health, significantly threatening system stability and reliability. To address the above challenge, we propose an anomaly detection model, ADmM, which integrates logs, metrics, and traces. ADmM first extracts template-level and semantic-level features from multimodal inputs. Then, a multi-scale autoencoder module is applied to impute missing metrics. For anomaly detection, the model represents microservice dependencies as a directed acyclic graph and leverages a graph neural network to learn generative patterns from normal system behavior. By measuring the deviation between observed values and reconstructed values, ADmM assigns anomaly scores to identify anomalies. Experiments conducted on three open-source benchmarks demonstrate that ADmM outperforms state-of-the-art methods across multiple anomaly detection metrics. Notably, it achieves F1-Score improvements of 5.77%, 5.48%, and 2.16% in scenarios with 40% incomplete metrics.

Knowledge graph embedding is efficient method for reasoning over known facts and inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based approaches learn the embedding of missing entities by a single triple only. They ignore the fact that the knowledge graph is essentially a graph structure. Graph-based methods consider graph structure information but ignore the contextual information of nodes in the knowledge graph, making them unable to discern valuable entity (relation) information. In response to the above limitations, we propose a general graph transformer framework for knowledge graph embedding (TGformer). It is the first to use a graph transformer to build knowledge embeddings with triplet-level and graph-level structural features in the static and temporal knowledge graph. Specifically, a context-level subgraph is constructed for each predicted triplet, which models the relation between triplets with the same entity. Afterward, we design a knowledge graph transformer network (KGTN) to fully explore multi-structural features in knowledge graphs, including triplet-level and graph-level, boosting the model to understand entities (relations) in different contexts. Finally, semantic matching is adopted to select the entity with the highest score. Experimental results on several public knowledge graph datasets show that our method can achieve state-of-the-art performance in link prediction.