Yang Zhao

Chang Zhou, Yang Zhao, Jin Cao et al.

This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost efficiency, demonstrating the model's applicability in real-world scenarios.

Chiyu Cheng, Chang Zhou, Yang Zhao et al.

The exponential growth of data storage demands has necessitated the evolution of hierarchical storage management strategies [1]. This study explores the application of streaming machine learning [3] to revolutionize data prefetching within multi-tiered storage systems. Unlike traditional batch-trained models, streaming machine learning [5] offers adaptability, real-time insights, and computational efficiency, responding dynamically to workload variations. This work designs and validates an innovative framework that integrates streaming classification models for predicting file access patterns, specifically the next file offset. Leveraging comprehensive feature engineering and real-time evaluation over extensive production traces, the proposed methodology achieves substantial improvements in prediction accuracy, memory efficiency, and system adaptability. The results underscore the potential of streaming models in real-time storage management, setting a precedent for advanced caching and tiering strategies.