Qingqing Liang — accepted AI-conference papers

2026

EdGCL: Disentangling Social and Cognitive Homophily in Graph-Based Educational Recommender Systems

Qingqing Liang, Chunyang Wang, Peiwei Xia, Yanan Zeng, Xin Liu, Xuesong Lu +1

AAAI 2026technical

Educational recommendation systems have been a fundamental component for alleviating learning disorientation in self-paced learning. While existing studies mainly leverage cognitive theories to guide learning motivation modeling, they critically overlook the role of social influences. Through empiri

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2026

LLM-Enhanced Knowledge and Learning Path Understanding for Graph-based Educational Recommendation

Qingqing Liang, Shuyan Zheng, Peiwei Xia, Chunyang Wang, Xuesong Lu, Aoying Zhou

IJCAI 2026

Educational recommendations empower personalized learning by suggesting suitable learning resources to learners, and the graph-based recommenders are widely adopted. Existing methods are mainly ID-based, which initialize learners and resources with trainable identifiers and optimize their representa

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