Understanding Generative Recommendation with Semantic IDs from a Model-scaling View

Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals. One popular GR paradigm is to use semantic IDs (SIDs), which are discrete codes quantized from the embeddings of modality encoders (e.g. large language or vision models), to represent items in an autoregressive user interaction sequence modeling setup (henceforth, SID-based GR). While generative models in other domains exhibit well-established scaling laws, our work reveals that SID-based GR shows significant bottlenecks while scaling up the model; in particular, the performance of SID-based GR quickly saturates as we enlarge each component — the modality encoder, the quantization tokenizer, and the RS itself. In this work, we identify the limited capacity of SIDs to encode item semantic information as one of the fundamental bottlenecks. Motivated by this observation, as an initial effort to obtain GR models with better scaling behaviors, we revisit another GR paradigm that directly uses textual representations of items (henceforth, Text-based GR). Our experiments show that Text-based GR paradigm has superior model scaling properties and achieves up to 20% improvement over the best achievable performance of SID-based GR through scaling. We also challenge the prevailing belief that LLMs struggle to capture collaborative filtering information, showing that LLMs' ability to model user–item interactions improves as LLMs scale up. Our analyses across model sizes from 44M to 14B parameters underscore the intrinsic scaling limits of SID-based GR and position Text-based GR as a promising path toward foundation models for GR.