AI前线·2025-12-09 06:26Core 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].