用语言模型估算新商品相似度，提升推荐系统冷启动表现
Language-Model Prior Overcomes Cold-Start Items
- 用语言模型分析商品描述，自动计算相似度作为先验知识
- 在两个真实数据集上显著提升推荐准确率，尤其对新商品效果明显
- 适用于电商、视频等快速更新内容的推荐场景
推荐系统的发展得益于数字化进程及对个性化内容的需求，如电商和视频流媒体领域。这些系统中的内容更新迅速，持续面临冷启动问题——新物品缺乏交互数据，难以评估价值。现有解决方案如基于内容的推荐和混合方法依赖结构化且信息丰富的元数据来判断物品相似性，但这类数据并不总能获取。本文提出一种新方法：利用语言模型估算物品间的相似度，并将其作为贝叶斯先验与经典推荐系统结合。该方法具有通用性，可提升多种推荐模型性能。实验将其集成到序列型与协同过滤型推荐器中，在两个真实数据集上验证，结果表明该方法显著改善了推荐效果，尤其在冷启动场景下。
原文摘要 · Abstract (English)
The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these systems often changes rapidly and therefore they constantly face the ongoing cold-start problem, where new items lack interaction data and are hard to value. Existing solutions for the cold-start problem, such as content-based recommenders and hybrid methods, leverage item metadata to determine item similarities. The main challenge with these methods is their reliance on structured and informative metadata to capture detailed item similarities, which may not always be available. This paper introduces a novel approach for cold-start item recommendation that utilizes the language model (LM) to estimate item similarities, which are further integrated as a Bayesian prior with classic recommender systems. This approach is generic and able to boost the performance of various recommenders. Specifically, our experiments integrate it with both sequential and collaborative filtering-based recommender and evaluate it on two real-world datasets, demonstrating the enhanced performance of the proposed approach.
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