語義ID用於聯合生成搜索與推薦

摘要

由大型語言模型（LLMs）驅動的生成模型正逐漸成為推薦與搜索任務的統一解決方案。這些模型中的一個關鍵設計選擇是如何表示項目，傳統上通過唯一識別符（IDs），而最近則使用由嵌入獲得的離散代碼組成的語義ID。雖然針對特定任務的嵌入模型可以提升單一任務的性能，但它們在聯合設置中可能無法很好地泛化。本文探討了在使用統一模型時，如何構建在搜索和推薦中均表現良好的語義ID。我們比較了多種構建語義ID的策略，包括針對特定任務和跨任務的方法，以及在聯合搜索與推薦生成模型中，每個任務是否應擁有自己的語義ID標記。我們的結果表明，使用在搜索和推薦任務上均進行微調的雙編碼器模型來獲取項目嵌入，隨後構建統一的語義ID空間，能夠提供有效的折衷方案，使兩項任務均表現出色。我們希望這些發現能激發後續關於可泛化、語義基礎的ID方案的研究，並為下一波統一的生成推薦架構提供啟示。

English

Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks. A key design choice in these models is how to represent items, traditionally through unique identifiers (IDs) and more recently with Semantic IDs composed of discrete codes, obtained from embeddings. While task-specific embedding models can improve performance for individual tasks, they may not generalize well in a joint setting. In this paper, we explore how to construct Semantic IDs that perform well both in search and recommendation when using a unified model. We compare a range of strategies to construct Semantic IDs, looking into task-specific and cross-tasks approaches, and also whether each task should have its own semantic ID tokens in a joint search and recommendation generative model. Our results show that using a bi-encoder model fine-tuned on both search and recommendation tasks to obtain item embeddings, followed by the construction of a unified Semantic ID space provides an effective trade-off, enabling strong performance in both tasks. We hope these findings spark follow-up work on generalisable, semantically grounded ID schemes and inform the next wave of unified generative recommender architectures.

語義ID用於聯合生成搜索與推薦

Semantic IDs for Joint Generative Search and Recommendation

摘要

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