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cs.AI2026
Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation
Ziyun Xu, Bosen Ding, Yue Zhang +10
Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.g., clicks, dwell time) rather tha…
cs.IR2026
ConAlign: Conditional Alignment Framework for Balancing Biased and Unbiased Recommendation
Jingcheng Zhang, Yihan Wang, Qi Song +1
Industry recommender systems trained on observational data suffer from various biases that create filter bubbles, causing user interests to collapse into narrow categories and seve…