activity
20242026
collaborators

14 papers

cs.IR2026

Agent4POI: Agentic Context-Conditioned Affordance Reasoning for Multimodal Point-of-Interest Recommendation

Jinze Wang, Yangchen Zeng, Tiehua Zhang +5

We introduce Agent4POI, the first POI recommendation framework that generates context-conditioned multimodal representations at recommendation time, rather than relying on static P…

cs.LG2026

Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learning

Yunhui Liu, Yongchao Liu, Yinfeng Chen +3

Hierarchical knowledge structures are ubiquitous across real-world domains and play a vital role in organizing information from coarse to fine semantic levels. While such structure…

cs.CL2026

Query as Anchor: Scenario-Adaptive User Representation via Large Language Model

Jiahao Yuan, Yike Xu, Jinyong Wen +9

Industrial-scale user representation learning requires balancing robust universality with acute task-sensitivity. However, existing paradigms primarily yield static, task-agnostic…

cs.AI2026

Text2GraphQuery-Bench: A Text to Graph Query Benchmark

Songlin Lyu, Lujie Ban, Zihang Wu +14

Graph models are fundamental to data analysis in domains rich with complex relationships. Unlike SQL, which benefits from a rel- atively unified standard and widespread familiarity…

cs.LG2026

Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering

Yunhui Liu, Pengyu Qiu, Yu Xing +6

Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. De…

cs.LG2026

UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks

Xiaotang Wang, Yun Zhu, Haizhou Shi +2

In the graph domain, deep graph networks based on Message Passing Neural Networks (MPNNs) or Graph Transformers often cause over-smoothing of node features, limiting their expressi…