34 citations · 54 across the 29 of their papers we have counts for
6 papers · 1 filter
Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
Jiancheng Wang, Mingjia Yin, Hao Wang +1
DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one hand, some works claim that…
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model
Wenjia Xie, Hao Wang, Luankang Zhang +3
Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior sequences. We revisit SR from a novel information-theoretic…
Entropy Law: The Story Behind Data Compression and LLM Performance
Mingjia Yin, Chuhan Wu, Yufei Wang +7
Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much…
Foundations and Frontiers of Graph Learning Theory
Yu Huang, Min Zhou, Menglin Yang +7
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network…
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation
Tingjia Shen, Hao Wang, Jiaqing Zhang +5
Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Trad…
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Likang Wu, Junji Jiang, Hongke Zhao +4
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved…