用用户交互数据训练文本模型，提升推荐系统表现
beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems
- 融合用户交互数据训练句向量模型，捕捉推荐场景特有模式
- 跨数据集迁移能力更强，优于传统协同过滤与语义相似模型
- 多领域联合训练可构建通用文本表示模型，适合冷启动推荐
推荐系统常利用文本信息提升预测效果，尤其在冷启动或零样本推荐场景下，传统协同过滤方法难以应用。近年来虽有不少基于文本挖掘的推荐方法，其中句向量模型（sentence Transformers）尤为突出，但这些模型通常仅针对语义相似性进行训练，未利用推荐系统中蕴含的交互数据隐含模式。本文提出beeFormer框架，通过引入用户交互数据来训练句向量模型。实验表明，使用beeFormer训练的模型具备跨数据集知识迁移能力，不仅超越了语义相似性句向量模型，也优于传统协同过滤方法。此外，我们在多个不同领域的数据集上联合训练，发现模型能累积知识，实现单一通用句向量模型对推荐系统的文本特征提取。代码、训练模型及实验细节已开源：https://github.com/recombee/beeformer。
原文摘要 · Abstract (English)
Recommender systems often use text-side information to improve their predictions, especially in cold-start or zero-shot recommendation scenarios, where traditional collaborative filtering approaches cannot be used. Many approaches to text-mining side information for recommender systems have been proposed over recent years, with sentence Transformers being the most prominent one. However, these models are trained to predict semantic similarity without utilizing interaction data with hidden patterns specific to recommender systems. In this paper, we propose beeFormer, a framework for training sentence Transformer models with interaction data. We demonstrate that our models trained with beeFormer can transfer knowledge between datasets while outperforming not only semantic similarity sentence Transformers but also traditional collaborative filtering methods. We also show that training on multiple datasets from different domains accumulates knowledge in a single model, unlocking the possibility of training universal, domain-agnostic sentence Transformer models to mine text representations for recommender systems. We release the source code, trained models, and additional details allowing replication of our experiments at https://github.com/recombee/beeformer.
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