Xingkong Ma — accepted AI-conference papers

2026

ProRec-Video: Guiding Hierarchical Interest Transitions for Proactive Short Video Recommendation with Dynamic Feedback Adaptation

Weizhi Chen, Baoyun Peng, Bo Liu, Xingkong Ma, Houjie Qiu

AAAI 2026technical

Traditional short video recommendations primarily enhance user retention by reinforcing existing user preferences, potentially leading to information cocoons. Conversely, proactive recommendations aim to diversify user interests by exposing users to content beyond their historical preferences. Howev

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2025

JI2S: Joint Influence‐Aware Instruction Data Selection for Efficient Fine‐Tuning

Jingyu Wei, Bo Liu, Tianjiao Wan, Baoyun Peng, Xingkong Ma, Mengmeng Guo

EMNLP 2025

Instruction tuning (IT) improves large language models (LLMs) by aligning their outputs with human instructions, but its success depends critically on training data quality, and datasets such as Alpaca often contain noisy or suboptimal examples that undermine fine‐tuning. Prior selection strategies

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