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Found 7 papers, 0 papers with code
Side Information-Driven Session-based Recommendation: A Survey
Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance.
Mixed Distillation Helps Smaller Language Model Better Reasoning
While large language models (LLMs) have demonstrated exceptional performance in recent natural language processing (NLP) tasks, their deployment poses substantial challenges due to high computational and memory demands in real-world applications.
Modeling User Viewing Flow Using Large Language Models for Article Recommendation
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles.
Optimal Propagation for Graph Neural Networks
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input.
Detecting Topology Attacks against Graph Neural Networks
In this work, we study the victim node detection problem under topology attacks against GNNs.
Ranking with Adaptive Neighbors
Despite success, these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix.
The Child is Father of the Man: Foresee the Success at the Early Stage
Understanding the dynamic mechanisms that drive the high-impact scientific work (e. g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources.
