Large language models are increasingly integrated into everyday and professional decision making, yet often exhibit sycophantic behavior by aligning with users’ views or preferences. While sycophancy can enhance interaction, its influence on users’ decisions remain unclear given different styles and task risks. We examine three forms of sycophancy—opinion agreement, direct praise, and self-deprecation—in two contrasting contexts: a low-risk speed-dating prediction task and a high-risk ETF investment task. In a 4×2 mixed-design online study (N = 106), we compare non-sycophantic AI with sycophantic variants on decision outcomes and confidence changes. Results show that sycophancy influences decision patterns in type-dependent ways. Specifically, opinion agreement reinforces initial decisions and self-deprecation boosts confidence. Interviews further indicate that users value supportive AI but question its objectivity when praise becomes excessive. These findings reveal the multifaceted effects of AI sycophancy and offer design implications for balancing support and credibility in human–AI interaction.

Personalized news recommendation is highly time-sensitive, as user interests are often driven by emerging events, trending topics, and shifting real-world contexts. These dynamics make it essential to model not only users' long-term preferences, which reflect stable reading habits and high-order collaborative patterns, but also their short-term, context-dependent interests that change rapidly over time. However, most existing approaches rely on a single static interaction graph, which struggles to capture both long-term preference patterns and short-term interest changes as user behavior evolves. To address this challenge, we propose a unified framework that learns user preferences from both global and local temporal perspectives. A global preference modeling component captures long-term collaborative signals from the overall interaction graph, while a local preference modeling component partitions historical interactions into stage-wise temporal subgraphs to represent short-term dynamics. Within this module, an LSTM branch models the progressive evolution of recent interests, and a self-attention branch captures long-range temporal dependencies. Extensive experiments on two large-scale real-world datasets show that our approach consistently outperforms strong baselines and delivers fresher and more relevant recommendations across diverse user behaviors and temporal settings.

Summary Fog computing is used as a popular extension of cloud computing for a variety of emerging applications. To incorporate various design choices and customized policies in fog computing paradigm, Microservices is proposed as a new software architecture, which is easy to modify and quick to deploy fog applications because of its significant features, ie, fine granularity and loose coupling. Unfortunately, the Microservices architecture is vulnerable due to its wildly distributed interfaces that are easily attacked. However, the industry has not been fully aware of its security issues. In this paper, a survey of different security risks that pose a threat to the Microservices‐based fog applications is presented. Because a fog application based on Microservices architecture consists of numerous services and communication among services is frequent, we focus on the security issues that arise in services communication of Microservices in four aspects: containers, data, permission, and network. Containers are often used as the deployment and operational environment for Microservices. Data is communicated among services and is vital for every enterprise. Permission is the guarantee of services security. Network security is the foundation for secure communication. Finally, we propose an ideal solution for security issues in services communication of Microservices‐based fog applications.