Core Viewpoint - The article discusses the launch of MiniOneRec, the first complete end-to-end open-source framework for generative recommendation, which validates the generative recommendation Scaling Law and provides a comprehensive training and research platform for the community [2][4]. Group 1: Generative Recommendation Framework - MiniOneRec has gained significant attention in the recommendation community since its release on October 28, with all code, datasets, and model weights open-sourced, requiring only 4-8 A100 GPUs for easy reproduction [6]. - The framework offers a one-stop lightweight implementation and improvement for generative recommendation, including a rich toolbox for SID (Semantic ID) construction, integrating advanced quantization algorithms [9]. - The framework has demonstrated a significant advantage in parameter utilization efficiency, as shown by the training and evaluation loss decreasing with increasing model size from 0.5 billion to 7 billion parameters [8][10]. Group 2: Performance Validation - Researchers have validated the generative recommendation Scaling Law on public datasets, showcasing the model's efficiency in parameter utilization [7]. - MiniOneRec outperforms traditional and generative recommendation paradigms significantly, leading by approximately 30 percentage points over the TIGER model in metrics such as HitRate@K and NDCG@K [23]. Group 3: Innovations in Recommendation - The framework introduces a full-process SID alignment strategy, which significantly enhances the performance of generative recommendations by incorporating world knowledge from large models [13][15]. - MiniOneRec employs a novel reinforcement learning strategy tailored for recommendations, including a constrained decoding sampling strategy to improve the diversity of generated items and a ranking reward to enhance the distinction of sorting signals [17][21]. Group 4: Future Outlook - The article raises the question of whether generative recommendation will become the new paradigm for recommendation systems, highlighting two approaches: the reformist approach, which integrates generative architecture into existing systems, and the revolutionary approach, which aims to completely overhaul traditional models [25][26]. - Both approaches have demonstrated the practical value of the generative paradigm, with some major companies already realizing tangible benefits from its implementation [27].