Artificial Intelligence 5 min read

Kuaishou and Tsinghua University Win First Prize in Qian Weichang Chinese Information Processing Award for Content Recommendation Technology

Kuaishou and Tsinghua University were honored with the first‑place Qian Weichang Chinese Information Processing Science and Technology Award for their collaborative content recommendation project, which achieved international‑level innovations in explainable recommendation, bias correction, and edge intelligence, and has been applied widely in Kuaishou's platform and top academic conferences.

Kuaishou Tech
Kuaishou Tech
Kuaishou Tech
Kuaishou and Tsinghua University Win First Prize in Qian Weichang Chinese Information Processing Award for Content Recommendation Technology

On November 29, the Chinese Society of Chinese Information Processing held its 2024 academic conference in Jiaxing, where the prestigious Qian Weichang Chinese Information Processing Science and Technology Award was announced.

Kuaishou and Tsinghua University jointly submitted the “Content Recommendation Related Technology Project” and secured the first prize, recognized for its high technical innovation, domestic leadership, and international advanced level.

Kuaishou Technology Vice President Jiang Peng and Tsinghua Professor Zhang Min attended the ceremony; the award highlighted years of close cooperation between Kuaishou’s commercial algorithm department and Tsinghua’s information retrieval group, praising the project’s high technical complexity, strong innovation, and leading international achievements in explainable recommendation.

In the era of digital intelligence, recommendation systems are a key channel for information access, facing challenges in accuracy, fairness, and explainability; both industry and academia have been leveraging AI, machine learning, and big data to improve them, echoing NVIDIA CEO Jensen Huang’s view that recommendation systems are among the most important AI systems today.

The award‑winning成果 includes breakthroughs such as long‑term value estimation, recommendation bias correction, and edge intelligence. Kuaishou addressed exposure and sample‑selection biases to enhance fairness and deployed intelligent re‑ranking models on end devices for real‑time user experience optimization.

These innovations have been deployed across Kuaishou’s main scenarios and published in top CCF‑A conferences like KDD, SIGIR, and WebConf, earning best‑paper awards and nominations.

As an AI‑driven technology company, Kuaishou remains committed to “technology for all,” will continue to increase R&D investment, and maintain close industry‑academic collaborations with institutions such as Tsinghua to advance China’s information technology and internet industry.

Artificial IntelligenceRecommendation systemscontent recommendationexplainabilityfairnessKuaishouTsinghua University
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