Hu Xiu·2025-06-25 00:48Core Viewpoint - Kuaishou has fully launched its AI model-driven recommendation system, OneRec, which is the first industrial-grade recommendation solution in the industry, setting a new standard globally [1][15]. Group 1: Technological Advancements - Kuaishou's technology has reached a top-tier level, particularly in video generation models [2]. - The company has made significant underlying technological advancements that surpass initial perceptions of it being merely a short video platform [3]. Group 2: Recommendation System Overview - Recommendation systems are a major technological innovation of the mobile internet era, utilized by popular platforms like Kuaishou, Douyin, and Pinduoduo [4]. - Traditional recommendation systems typically rely on user-based collaborative filtering and content-based collaborative filtering [4][6]. Group 3: Challenges in Traditional Systems - Traditional multi-stage recommendation systems face issues such as low overall GPU utilization and inefficiencies due to independent model operations [10][11]. - The complexity of user interests and the conflicting goals of increasing click-through rates while maintaining content diversity lead to decreased recommendation accuracy [9][10]. Group 4: OneRec's Innovations - OneRec shifts from a multi-stage filtering approach to an end-to-end model that directly generates a list of recommended videos based on user interests [16]. - The system employs a multi-modal semantic tokenizer to deeply understand video content beyond surface-level tags, enhancing content comprehension [21][24]. Group 5: User Modeling and Interest Tracking - OneRec integrates user behavior over time to create a comprehensive "interest sequence," allowing for more accurate recommendations that adapt to changing user preferences [28][30]. - The model uses deep neural networks to automatically learn complex interest changes from large datasets, improving recommendation accuracy [30]. Group 6: Recommendation Generation - The system utilizes an encoder-decoder structure, where the encoder compresses user interest trajectories into vectors, and the decoder generates a sequence of recommended content [32][33]. - The introduction of a Mixture of Experts (MoE) architecture enhances model capacity and efficiency, allowing for personalized recommendations while maintaining content diversity [34][36]. Group 7: Reinforcement Learning Integration - OneRec incorporates a reward mechanism using reinforcement learning to align user preferences with recommendation outcomes, enhancing the overall effectiveness of the system [38][44]. - The model's training includes various reward signals to ensure a balanced distribution of content types and to adapt to real-world business complexities [41][42]. Group 8: Performance Metrics - During the testing phase, OneRec demonstrated performance metrics comparable to existing complex systems, with user engagement metrics such as watch time and user lifecycle showing positive growth [46][47]. - In local life scenarios, OneRec achieved a 21% increase in GMV and significant growth in order volume and new customer acquisition [48]. Group 9: Future Considerations - Despite its advancements, OneRec still faces challenges related to inference speed, resource consumption, and further optimization of the reward mechanism [49]. - The introduction of OneRec marks a new phase in recommendation systems, aligning them with the latest advancements in AI and machine learning [49][50].