Text-Attributed Graphs (TAGs) are prevalent in various real-world scenarios, where each node is associated with a text attribute. Representation learning on TAGs relies on a comprehensive understanding of both the textual attributes and the topological connections. Recent works have enhanced graph neural networks (GNNs...
Hao Yan, Chao-Zhuo Li, Jun Yin et al.· ACM Transactions on Intellig...· 0 citations
This study proposes a novel GR framework, named TDPM, by designing the time-aware diffusion on SID tokens, which explicitly integrates the impact of time-evolving user preferences into the diffusion process.
This paper proposes the End-to-End Dual Dynamic (ED2) recommender, the first LLM-based sequential RS which adopts dual dynamic index mechanism, targeting resolving the above limitations simultaneously.
Jun Yin, Zhengxin Zeng, Mingzheng Li et al.· The Web Conference· 27 citations
Noise Corruption is introduced, a Noise Corruption-based explanation framework, which perturbs each message through matched-norm random-direction corruption while preserving the expected squared message norm, and NICE, a Noise Corruption-based explanation framework, which learns a Stochastic Restoration Boundary under...
Ziluowen Luo, Jun Yin, Ruo-Chen Liu et al.· 0 citations
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