小红书RecSys 2025最佳论文提名背后：破解视频时长预测难题

Core Insights - The article highlights the impressive capabilities of Xiaohongshu's recommendation system, which has gained recognition at the RecSys 2025 conference for its innovative research and technology [4][6][7]. Group 1: Xiaohongshu's Recognition - Xiaohongshu's recommendation algorithm team received a "Best Paper Candidate" nomination at the prestigious RecSys 2025 conference for their paper on video watch time prediction [4][6]. - The conference is recognized as a leading academic event in the field of recommendation systems, attracting top scholars and industry experts from around the world [6][7]. - Xiaohongshu's technology and product have become focal points at the conference, with many attendees praising its recommendation capabilities as industry-leading [9][10]. Group 2: Research and Methodology - The paper titled "Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network" addresses the critical issue of predicting user watch time, which is essential for enhancing user engagement [17][22]. - The research identifies complex user behavior patterns in watch time, highlighting the challenges of skewed distributions and diverse viewing habits [30][31]. - The proposed Exponential-Gaussian Mixture Network (EGMN) model combines classic probability distributions to predict the complete probability distribution of watch time rather than a single value [33][35]. Group 3: Performance and Validation - EGMN demonstrated superior performance in offline experiments, achieving a 14.11% reduction in Mean Absolute Error (MAE) and a 7.76% increase in ranking consistency [39]. - Online A/B testing covering 15 million users over seven days showed significant improvements in key metrics, with a 19.94% decrease in KL divergence, indicating strong distribution fitting capabilities [40][41]. - Ablation studies confirmed the effectiveness of EGMN's components, validating the contributions of both the exponential and Gaussian components to the model's performance [42]. Group 4: Future Directions - The article emphasizes Xiaohongshu's commitment to a pragmatic approach in technology development, focusing on real user problems and continuous exploration of cutting-edge recommendation algorithms [46][47]. - The success at RecSys 2025 is seen as a starting point for further advancements in the recommendation system field, with the team actively seeking talent to enhance their research efforts [47].