Core Insights - The article discusses the transformation of search, advertising, and recommendation systems through the integration of large models, emphasizing the challenges and solutions for implementing generative recommendations in practical scenarios [2][4]. Group 1: Key Changes in Search and Recommendation Systems - The most significant change brought by large models is in feature engineering, where traditional methods are being enhanced by the capabilities of large language models to extract richer features from vast amounts of data [6]. - The industry is still far from achieving a fully unified end-to-end pipeline, with most efforts focused on integrating large models into specific points of the pipeline rather than complete reconstruction [12][4]. - The scaling law remains applicable in recommendation systems, indicating that the marginal benefits of model scaling have not yet reached their limits, particularly due to the vast amount of user behavior data available [13][17]. Group 2: Challenges and Solutions in Model Implementation - A major challenge in deploying large models is the need for extensive foundational work, such as data cleaning and sample construction, which can consume significant time and resources [8]. - The transition from traditional feature engineering to a more systematic approach to data and sample construction is crucial for realizing the potential of large models [8][9]. - Balancing model size, performance, and computational costs is essential, with smaller models being preferred in low-value scenarios while larger models are pursued for high-value applications [19][20]. Group 3: Future Directions and Innovations - The future of recommendation systems may see a shift from feature engineering to knowledge engineering, where models learn directly from raw user behavior data supplemented by incremental knowledge [30]. - The development of intelligent agents capable of autonomous planning and execution of complex tasks is anticipated, moving beyond predefined workflows [30]. - The industry is encouraged to focus on maximizing the utility of existing models by improving the quality of training data and optimizing the model's effective parameters [20][38].

Core Viewpoint - The article discusses the significant impact of large models on recommendation systems, emphasizing that these models have already generated tangible benefits in the industry rather than focusing on future possibilities or academic discussions [1]. Group 1: Impact of Large Models on Recommendation Systems - Large models have transformed the way knowledge is learned, shifting from a closed system reliant on internal data to an open system that integrates vast external knowledge [4]. - The structure of large models, typically based on transformer architecture, differs fundamentally from traditional recommendation models, which raises questions about whether they can redefine the recommendation paradigm [5]. - Large models have the potential to create a "new world" by enabling personalized content generation, moving beyond mere recommendations to directly creating tailored content for users [6]. Group 2: Knowledge Input Comparison - A comparison highlights that large models draw knowledge from an open world, while traditional systems rely on internal user behavior data, creating a complementary relationship [7]. - Large models possess advantages in knowledge quantity and embedding quality over traditional knowledge graph methods, suggesting they are the optimal solution for knowledge input in recommendation systems [8]. Group 3: Implementation Strategies - Two primary methods for integrating large model knowledge into recommendation systems are identified: generating embeddings from large language models (LLMs) and producing text tokens for input [10][11]. - The integration of multi-modal features through large models allows for a more comprehensive representation of item content, enhancing recommendation capabilities [13][15]. Group 4: Evolution of Recommendation Models - The exploration of large models in recommendation systems has progressed through three stages, from initial toy models to more industrialized solutions that significantly improve business metrics [20][24]. - Meta's generative recommendation model (GR) exemplifies a successful application of large models, achieving a 12.4% increase in core business metrics by shifting the focus from click-through rate prediction to predicting user behavior [24][26]. Group 5: Content Generation and Future Directions - The article posits that the most profound impact of large models on recommendation systems lies in the personalized generation of content, integrating AI creators into the recommendation process [28][29]. - Current AI-generated content still requires human input, but the potential for fully autonomous content generation based on user feedback is highlighted as a future direction [41][43]. Group 6: Industry Insights and Recommendations - The search and recommendation industry is viewed as continuously evolving, with the integration of large models presenting new growth opportunities rather than a downturn [45]. - The article suggests that the key to success in the next phase of recommendation systems lies in the joint innovation and optimization of algorithms, engineering, and large models [46].