南京大学学报（自然科学版）2025，Vol.61Issue(4)：660-671,12.DOI:10.13232/j.cnki.jnju.2025.04.011
基于可解释嵌入学习的推荐系统
Explainable embedding learning for recommender systems
摘要
Abstract
Latent factor models aim to learn implicit embeddings of users and items from historical behavior data,serving as a core technology in modern recommender systems.However,the lack of interpretability in implicit embeddings significantly limits the trustworthiness of recommendations.To this end,we propose a novel method called Prompt Ensemble-based Explainable Embedding for Review-aware Rating Regression(PE3R3),which jointly leverages textual reviews and numerical ratings to learn explicit embeddings with clear semantics,thereby enhancing the interpretability of recommendations.First,PE3R3 employs pre-trained language models with diverse prompt templates to extract meta codebooks with explicit semantics from textual reviews.Then,using numerical ratings as supervisory signals,PE3R3 represents users and items as linear combinations of multiple meta codes through a residual quantization mechanism,yielding explicit embeddings rich in semantics and enabling interpretable recommendations.PE3R3 is a plug-and-play method,allowing seamless integration with existing rating regression models.Experimental results show that PE3R3 yields an average improvement of 5%and a maximum improvement of 16%in predictive accuracy.In addition,both quantitative analysis and qualitative analysis demonstrate that incorporating PE3R3 effectively enhances the interpretability of recommendations.关键词
可解释推荐/隐因子模型/提示学习/评论感知评分回归/向量量化Key words
explainable recommendation/latent factor model/prompt learning/review-aware rating regression/vector quantization分类
信息技术与安全科学引用本文复制引用
李雅静,卢香葵,刘林,邬俊..基于可解释嵌入学习的推荐系统[J].南京大学学报（自然科学版）,2025,61(4):660-671,12.基金项目
数字化学习技术集成与应用教育部工程研究中心创新基金重点项目(1321004),北京市自然科学基金(L232033) （1321004）
