融合异质信息网络表示学习的跨领域推荐研究

doi:10.3772/j.issn.1000-0135.2022.04.002

情报学报

2022, Vol. 41

Issue (4): 337-349 DOI: 10.3772/j.issn.1000-0135.2022.04.002

情报理论与方法

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融合异质信息网络表示学习的跨领域推荐研究

易明, 刘明, 冯翠翠

华中师范大学信息管理学院，武汉 430079

Cross-Domain Recommendation Combining Heterogeneous Information Network Representation Learning

Yi Ming, Liu Ming, Feng Cuicui

School of Information Management, Central China Normal University, Wuhan 430079

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摘要针对单领域推荐中的数据“稀疏性”和用户“冷启动”问题，提出一种综合利用评分信息和特征信息的跨领域推荐模型。首先，利用异质网络表示学习，针对源领域和目标领域的异质信息网络，通过元路径、DeepWalk算法生成网络表示学习向量，进而利用个性化非线性融合输出源领域和目标领域的物品特征信息向量；其次，利用神经网络模拟CMF（collective matrix factorization），生成用户和物品的评分信息向量，并通过映射函数MLP（multilayer perceptron）将用户评分信息向量映射到不同领域，以突出用户特征在不同领域的差异性；最后，将评分信息和特征信息有机融合，以损失函数为依据，采用梯度下降的方法学习模型的参数，从而完成评分预测。研究结果表明，在豆瓣网和Amazon数据集上，本文模型均优于其他相关算法；在提升推荐效果方面，目标领域RMSE（root mean squared error）和MAE（mean absolute error）下降了1%~15%，源领域RMSE和MAE下降了1%~19%；在用户“冷启动”方面，目标领域的RMSE和MAE下降了1%~14%。

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	易明
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关键词 ：跨领域推荐, 异质网络表示学习, 联合矩阵分解

收稿日期: 2021-01-14

基金资助:国家社会科学基金重大项目“新时代我国文献信息资源保障体系重构研究”（19ZDA345）。

作者简介: 易明，男，1978年生，博士，教授，博士生导师，主要研究方向为信息行为与个性化服务，E-mail：yiming0415@mail.ccnu.edu.cn；刘明，男，1994年生，硕士研究生，主要研究方向为个性化推荐；冯翠翠，女，1994年生，博士研究生，主要研究方向为用户信息行；

引用本文:

易明, 刘明, 冯翠翠. 融合异质信息网络表示学习的跨领域推荐研究[J]. 情报学报, 2022, 41(4): 337-349.
Yi Ming, Liu Ming, Feng Cuicui. Cross-Domain Recommendation Combining Heterogeneous Information Network Representation Learning. 情报学报, 2022, 41(4): 337-349.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2022.04.002 或 https://qbxb.istic.ac.cn/CN/Y2022/V41/I4/337

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