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cs.IR2026
Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders
Jun Yin, Bangguo Zhu, Peng Huo +5
Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the recommendation paradigm. Despite…
cs.IR2026
UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation
Xinghe Cheng, Guiyong Zhuang, Yusheng Xie +5
Personalized exercise recommendation dynamically aligns pedagogical resources with individual knowledge mastery, which is crucial for satisfying students' dynamic learning needs in…