解决推荐系统冷启动问题，用内容嵌入提升新商品推荐效果
DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation
- 双路径嵌入：将内容向量映射到物品ID空间，实现无缝泛化
- 在三个真实数据集上优于纯ID模型，无需调参且使用轻量模型
- 适合动态商品目录场景，尤其对新商品推荐有明显提升
基于Transformer的序列推荐模型（如SASRec或BERT4Rec）通常仅依赖学习到的物品ID嵌入，易受物品冷启动问题影响，尤其在动态商品目录环境中。尽管预训练模型提供的密集内容嵌入具有潜力，但直接集成到Transformer推荐模型中表现始终不如纯ID方法。本文重新审视这一挑战，提出DenseRec，一种简单有效的双路径嵌入方法。DenseRec在训练过程中学习从密集嵌入空间到ID嵌入空间的线性投影，实现对未见物品的无缝泛化，无需专用嵌入模型或复杂基础设施。在三个真实世界数据集上的实验表明，DenseRec始终优于仅使用ID的SASRec基线，且无需额外超参数调优，同时使用紧凑嵌入模型。分析显示，性能提升主要源于在存在未见物品时更优的序列表示，使DenseRec成为冷启动序列推荐的实用且稳健解决方案。
原文摘要 · Abstract (English)
Transformer-based sequential recommenders, such as SASRec or BERT4Rec, typically rely solely on learned item ID embeddings, making them vulnerable to the item cold-start problem, particularly in environments with dynamic item catalogs. While dense content embeddings from pre-trained models offer potential solutions, direct integration into transformer-based recommenders has consistently underperformed compared to ID-only approaches. We revisit this integration challenge and propose DenseRec, a simple yet effective method that introduces a dual-path embedding approach. DenseRec learns a linear projection from the dense embedding space into the ID embedding space during training, enabling seamless generalization to previously unseen items without requiring specialized embedding models or complex infrastructure. In experiments on three real-world datasets, we find DenseRec to consistently outperform an ID-only SASRec baseline, even without additional hyperparameter tuning and while using compact embedding models. Our analysis suggests improvements primarily arise from better sequence representations in the presence of unseen items, positioning DenseRec as a practical and robust solution for cold-start sequential recommendation.
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