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.

In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.

Recommender systems have been studied comprehensively in both academic and industrial fields over the past decade. As user interests can be affected by context at any time and any place in mobile scenarios, rich context information becomes more and more important for personalized context-aware recommendations. Although existing context-aware recommender systems can make context-aware recommendations to some extent, they suffer several inherent weaknesses: (1) Users’ context-aware interests are not modeled realistically, which reduces the recommendation quality; (2) Current context-aware recommender systems ignore trust relations among users. Trust relations are actually context-aware and associated with certain aspects (i.e., categories of items) in mobile scenarios. In this article, we define a term role to model common context-aware interests among a group of users. We propose an efficient role mining algorithm to mine roles from a “user-context-behavior” matrix, and a role-based trust model to calculate context-aware trust value between two users. During online recommendation, given a user u in a context c , an efficient weighted set similarity query (WSSQ) algorithm is designed to build u ’s role-based trust network in context c . Finally, we make recommendations to u based on u ’s role-based trust network by considering both context-aware roles and trust relations. Extensive experiments demonstrate that our recommendation approach outperforms the state-of-the-art methods in both effectiveness and efficiency.