让语言模型的文本嵌入更适配推荐系统，提升推荐效果。
EncodeRec: An Embedding Backbone for Recommendation Systems
- 冻结语言模型参数，用推荐目标优化嵌入空间。
- 在多个推荐基准上超越传统语言模型和嵌入模型。
- 适合需要高效、精准文本表示的推荐场景。
当前推荐系统越来越多地使用大预训练语言模型（PLM）的嵌入表示。然而，这些嵌入存在两个关键局限：(1) PLM未显式优化以生成结构化且区分度高的嵌入空间；(2) 表示过于通用，难以捕捉推荐任务所需领域特定语义。我们提出EncodeRec，一种将文本表示对齐推荐目标的方法，直接从商品描述中学习紧凑且信息丰富的嵌入。训练推荐系统时冻结语言模型参数，实现高效计算且不损失语义保真度。在核心推荐基准上的实验表明，EncodeRec作为序列推荐模型的骨干或语义ID标记化方法，均显著优于基于PLM和嵌入模型的基线。结果凸显了嵌入适配在弥合通用语言模型与实际推荐系统差距中的关键作用。
原文摘要 · Abstract (English)
Recent recommender systems increasingly leverage embeddings from large pre-trained language models (PLMs). However, such embeddings exhibit two key limitations: (1) PLMs are not explicitly optimized to produce structured and discriminative embedding spaces, and (2) their representations remain overly generic, often failing to capture the domain-specific semantics crucial for recommendation tasks. We present EncodeRec, an approach designed to align textual representations with recommendation objectives while learning compact, informative embeddings directly from item descriptions. EncodeRec keeps the language model parameters frozen during recommender system training, making it computationally efficient without sacrificing semantic fidelity. Experiments across core recommendation benchmarks demonstrate its effectiveness both as a backbone for sequential recommendation models and for semantic ID tokenization, showing substantial gains over PLM-based and embedding model baselines. These results underscore the pivotal role of embedding adaptation in bridging the gap between general-purpose language models and practical recommender systems.
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