AI 中文总结
探讨推荐系统如何利用信息，分析原始ID主导早期发展及语义信息封装于ID的原因，引入语义规划为未来方向，指出转变需在模型设计、评估及协调多方目标等方面做出改变。
AI 中文摘要
推荐系统的发展可通过探究其如何大规模利用信息来探索。在过去二十年的大部分时间里，工业系统依赖原始ID，它是离散、全球唯一且语义不透明的标识符，能实现大规模精确查找、记录和特定项目记忆。随着时间推移，推荐系统寻求利用更丰富信息源。这种发展导致新阶段，部分信息不再仅作为围绕项目标识的辅助特征，而是越来越多地封装在语义ID中。本文审视过去、现在和未来，探讨三个相关问题：为何原始ID主导推荐系统早期发展，为何语义信息如今越来越多地封装在ID中，以及推荐超越语义检索后会怎样。特别引入语义规划作为未来可能方向，系统先预测下一次曝光的语义目标，再将其实例化为特定项目或生成创意。还认为这种转变不仅需要模型设计改变，还需评估及推荐系统协调用户、平台和提供商目标方式的改变。
英文摘要
The evolution of recommender systems can be explored by asking how they utilize information at scale. Throughout most of the historical period under consideration during the past two decades, industrial systems have relied on raw IDs, which are discrete, globally unique, and semantically opaque identifiers that enable exact lookup, logging, and item-specific memorization at scale. Over time, however, recommender systems have sought to utilize richer sources of information, including item content, context, multimodal signals, and cross-domain structure. This development has led to a new stage in which part of such information is no longer used solely as auxiliary features around item identity, but is increasingly encapsulated in semantic IDs that provide a more structured, model-facing form of identity. We argue that this shift goes beyond the rise of generative recommendation over traditional methods. Indeed, it reflects a broader evolution in how recommender systems utilize information under industrial-scale constraints. This paper looks at the past, present, and future to examine three connected questions: why raw IDs dominated the early development of recommender systems, why semantic information is increasingly being encapsulated in IDs today, and what may come next once recommendations move beyond semantic retrieval. In particular, we introduce semantic planning as a possible future direction in which the system first predicts the semantic target of the next exposure, and only then instantiates that target as a specific item or generated creative. We further argue that such a shift may require changes not only in model design but also in evaluation and in the way recommender systems coordinate the objectives of users, platforms, and providers.
