计算机应用研究2024，Vol.41Issue(7)：2025-2032,8.DOI:10.19734/j.issn.1001-3695.2023.11.0547
基于元学习的多视图对比融合冷启动推荐算法
Multi-view contrast fusion cold start recommendation algorithm based on meta-learning
摘要
Abstract
Addressing the challenges faced by current cold start recommendation models in effectively mining structural and semantic information in heterogeneous information networks,and their tendency to overlook user behavior attributes within these networks,this paper introduced a meta-learning-based multi-view contrast fusion cold start recommendation algorithm(MVC-ML).This algorithm effectively tackled the cold start problem at both the model and data layers.Within the MVC-ML framework,it firstly extracted higher-order semantic information from heterogeneous information networks using a meta-path view.Subsequently,it captured the network's structural features using a network pattern view.Following this,the algorithm analyzed user behavior attribute information through a clustering view.Finally,MVC-ML employed a contrast learning method to integrate the information extracted from these three views,thus generating accurate representation vectors.Experimental validations on datasets,including DBook,demonstrate that the MVC-ML model,in a cold start scenario,reduces MAE by 1.67％,lowers RMSE by 2.06％,and increases nDCG@K by 1.48％compared to traditional heterogeneous information net-work models such as MetaHIN.These results fully confirm the rationality and effectiveness of the MVC-ML algorithm.关键词
异质信息网络/对比学习/网络模式/冷启动Key words
heterogeneous information network/contrast learning/network mode/cold start分类
信息技术与安全科学引用本文复制引用
张子扬,刘小洋..基于元学习的多视图对比融合冷启动推荐算法[J].计算机应用研究,2024,41(7):2025-2032,8.基金项目
重庆市社科联重点项目(2023NDZD09) （2023NDZD09）
重庆市教委人文社科重点项目(23SKGH247) （23SKGH247）
