Guaranteed display advertising is crucial for platform monetization, yet existing methods often operate under a single-slot assumption, limiting their ability to optimize allocation across multi-slot page views. In this paper, we propose a novel joint optimization framework for multi-slot GD allocation, addressing key challenges such as slot-level redundancy, contract imbalance, and exposure concentration. Our approach formulates the allocation as an offline bipartite matching problem with a contract roulette mechanism for slot exclusivity and Page View constraints for impression control, and incorporates a scalable allocation optimization algorithm for efficient large-scale deployment. Extensive online tests on the Meituan advertising platform demonstrate that our method significantly improves merchant ROI, platform revenue efficiency, and contract fulfillment robustness. Specifically, online A/B tests show a 28.99% increase in Average Revenue Per User under 70% traffic, and DID analysis further indicates improved contract stability, demonstrating the strong applicability and effectiveness of our framework in real-world advertising deployments.

The anchor-based multi-view clustering is a prominent focus in massive data analysis. Although it has witnessed considerable progress, two critical limitations can still be observed in recent research. First, most existing clustering frameworks construct one anchor graph independently for each view, which may which may overlook the versatile diversity across multi-view latent distributions. In additional, they typically suffer from the two-stage issue, further restricting their flexibility in complex practical scenarios. By considering of this, we derive a new Multi-view Clustering approach via Flexible Dual-level Fusion (MC-FDF). Unlike the traditional one-anchor-graph-per-view practice, the proposed approach first produces a series of diversified anchor graphs from multiple views. Subsequently, these diversified anchor graphs are further fused from a scale-aware perspective, followed by the rotation of scale alignments to capture the consistent clustering structure across different views. With multiple views extended to the multi-scale multi-view paradigm, two levels of granularity (i.e. diversified anchor graph fusion and multi-scale late fusion) are seamlessly formulated into a mutually beneficial framework, in especially a facilitated optimization algorithm with linear time complexity is also provided. Comprehensive evaluations on heterogeneous benchmark datasets confirm that the proposed approach achieves superior performance and scalability over the state-of-the-art baselines.

Content creators face heightened risks of online harm due to their public visibility and the increasing hostility in digital spaces. Traditional moderation tools, such as reporting and deletion, often fall short in addressing the emotional and reputational impact of online hate. In this study, we explore how community-driven approaches like counterspeech may support creators in navigating hateful comments, and how AI-powered tools could assist in this process. We conducted qualitative interviews and concept testing with 15 active content creators from Chinese social media platforms to understand their experiences and expectations. Our findings reveal that creators' approaches to counterspeech are shaped by their professional roles, the need to maintain authenticity, and the pressures of audience management. While creators saw value in AI-assisted counterspeech for improving efficiency and tone, they expressed concerns about misrepresentation, loss of control, and unintended harm. They preferred AI tools that augmented their voice rather than replaced it, and emphasized the importance of balancing emotional nuance with scalability. These insights inform the design of creator-centered AI moderation tools that integrate human agency and context sensitivity, contributing to healthier and more sustainable online environments.