Yang Song
12 papers in the PaperMetrix corpus
Papers by this author
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A data-driven storage recommendation service for multitenant storage management environments
2015
Storage management aims to improve the data center performance by optimizing the underlying storage resources more efficiently. The advent of cloud computing technologies introduces a paradigm shift from conventional on-premise storage management solutions to multitenant …
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Permutation Invariant Graph Generation via Score-Based Generative Modeling
2020 · arXiv (Cornell University)
Learning generative models for graph-structured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of nodes. However, most of the existing generative models for graphs are …
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A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics
2019
In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, such as fault prediction and yield prediction. However, …
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A Test-Driven Approach to Improving Student Contributions to Open-Source Projects
2019
Test-driven development (TDD) promises to help students write high-quality code with fewer defects. Although many studies of TDD usage have been conducted in entry-level computer science courses, few have looked at more advanced students doing …
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HybridGNN: Learning Hybrid Representation for Recommendation in Multiplex Heterogeneous Networks
2022 · 2022 IEEE 38th International Conference on Data Engineering (ICDE)
Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse interactions among nodes and abundant semantics emerging from diverse types of nodes …
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Recommending Bug Assignment Approaches for Individual Bug Reports: An Empirical Investigation
2023 · arXiv (Cornell University)
Multiple approaches have been proposed to automatically recommend potential developers who can address bug reports. These approaches are typically designed to work for any bug report submitted to any software project. However, we conjecture that …
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Mixed Attention Network for Cross-domain Sequential Recommendation
2023 · arXiv (Cornell University)
In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which …
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GLOBE: A High-quality English Corpus with Global Accents for Zero-shot Speaker Adaptive Text-to-Speech
2024 · arXiv (Cornell University)
This paper introduces GLOBE, a high-quality English corpus with worldwide accents, specifically designed to address the limitations of current zero-shot speaker adaptive Text-to-Speech (TTS) systems that exhibit poor generalizability in adapting to speakers with accents. …
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Trigger3:Refining Query Correction via Adaptive Model Selector
2025 · Proceedings of the AAAI Conference on Artificial Intelligence
In search scenarios, user experience can be hindered by erroneous queries due to typos, voice errors, or knowledge gaps. Therefore, query correction is crucial for search engines. Current correction models, usually small models trained on …
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A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
2015
Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services …
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Sequential Recommendation with Graph Neural Networks
2021
Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation. First, user behaviors in their rich historical sequences are often …
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Disentangling Long and Short-Term Interests for Recommendation
2022 · Proceedings of the ACM Web Conference 2022
Modeling user’s long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may …