现代情报2024，Vol.44Issue(9)：31-41,11.DOI:10.3969/j.issn.1008-0821.2024.09.003
融合跨平台用户偏好与异质信息网络的推荐算法研究
Research on Recommendation Algorithm Integrating Cross-Platform User Preferences and Heterogeneous Information Networks
摘要
Abstract
[Purpose/Significance]This paper proposes a recommendation algorithm that integrates cross-platform us-er preferences and heterogeneous information networks,based on the heterogeneous big data of cross-platform users.It plays a significant role in alleviating the sparsity and cold start problems of personalized recommendation.[Method/Process]Initially,the paper constructed a user core interest social circle based on cross-platform heterogeneous informa-tion,captured user information preference features in both the source and target platforms through convolutional neural net-works and self-attention mechanisms.Subsequently,it built a heterogeneous information network based on the core interest network and the relationships among recommended items,and it employed a heterogeneous graph attention network model for feature aggregation.Finally,the study integrated the above feature embeddings into an improved matrix factorization model to compute recommendation scores.[Results/Conclusion]The model demonstrates superior performance across four independently constructed cross-platform datasets.This study not only fills the gap in cross-platform,multi-attribute,and fine-grained datasets in the field of recommendation but also enhances the theoretical and methodological system related to recommendation by introducing cross-platform features.关键词
推荐算法/跨平台/异质信息网络/用户偏好/深度学习Key words
recommendation algorithm/cross-platform/heterogeneous information networks/user preferences/deep learning分类
社会科学引用本文复制引用
张雪,毕达天,陈功坤,杜小民..融合跨平台用户偏好与异质信息网络的推荐算法研究[J].现代情报,2024,44(9):31-41,11.基金项目
国家社会科学基金项目"基于用户跨社交媒体的信息行为偏好特征挖掘与推荐研究"(项目编号:21BTQ059). （项目编号:21BTQ059）
