Xin Lang Cai Jing·2025-11-11 06:35Core Insights - Kuaishou has developed multiple large models this year, including OneRec for recommendation systems, OneSearch for e-commerce search, and a generative reinforcement learning bidding technology for commercialization [1][10] - The company aims to enhance user experience and improve merchant efficiency through the application of these large models in core business scenarios [1][10] Recommendation System - Kuaishou's self-developed OneRec model innovates in multi-modal representation alignment, addressing the inadequacies of open-source models in extracting relationships from private recommendation data [2][4] - The OneRec model has undergone three iterations, significantly improving user engagement metrics: OneRec-V1 increased average user stay time by 0.5% and 1.17% for Kuaishou App and Kuaishou Lite respectively, while reducing the proportion of marketing accounts in recommended content [4][5] - Subsequent versions, OneRec-V2 and OneRec-Think, further enhanced user engagement, with OneRec-V2 increasing stay time by an additional 0.46% and 0.74% [4][5] E-commerce Search - Kuaishou's OneSearch model replaces traditional e-commerce search architectures, addressing issues like semantic confusion and incomplete understanding of user intent [5][9] - The implementation of OneSearch has led to a 2.3% increase in click-through rates on search pages, a reduction in decision-making time to one-third of traditional methods, and over a 40% increase in exposure for quality long-tail products [9] Commercialization - Kuaishou has introduced generative reinforcement learning bidding technology, which analyzes a series of bids and feedback to optimize decision-making based on ROI and customer acquisition costs [9][10] - The company emphasizes the importance of integrating AI technology with business scenarios to drive core business benefits [10][11]