IRJul 10

From Raw IDs to Semantic Planning: How Recommender Systems Utilize Information at Scale

arXiv:2607.095404.3
Predicted impact top 85% in IR · last 90 daysOriginality Synthesis-oriented
AI Analysis

For researchers and practitioners in recommender systems, this paper provides a conceptual framework and future direction, but it is primarily a survey/position paper without empirical results.

This paper traces the evolution of recommender systems from raw ID-based approaches to semantic IDs and proposes semantic planning as a future direction where systems predict semantic targets before instantiating items. It argues that this shift represents a broader change in how information is utilized at scale.

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.

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