Large Language Model (LLM)-based generative frameworks are transforming how we approach both search and recommendation. Rather than maintaining separate, task-specific systems, these models provide a unified solution that simplifies design and can improve generalization across tasks. A central challenge, however, lies in how to represent items as discrete tokens that LLMs can process. Traditional approaches, such as using unique IDs, sequential IDs, or item titles, face limitations in scalability and in handling cold-start items.

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Spotify Research

Researchers at Spotify developed a multi-task approach for creating Semantic IDs that work effectively in unified generative models for both search and recommendation. Traditional task-specific Semantic IDs fail to generalize across both functions, but their proposed bi-encoder model jointly fine-tuned for both tasks achieves balanced performance. The multi-task Semantic IDs represent items as discrete tokens based on embeddings that incorporate information from both search queries and recommendation patterns, offering a promising direction for more generalizable generative recommender systems.

Semantic IDs for Generative Search and Recommendation

Task-specific semantic IDs do not generalize in a joint generative Search & Recommendation (S&R) model

Semantic IDs from a multi-task bi-encoder model