融合评论文本特征和评分图卷积表示的推荐模型  

Recommendation model combining review’s feature and rating graph convolutional representation

作　　者：冯海林[1] 张潇 刘同存 FENG Hailin;ZHANG Xiao;LIU Tongcun(College of Mathematics and Computer Science,Zhejiang A&F University,Hangzhou 311300,China)

机构地区：[1]浙江农林大学数学与计算机科学学院,浙江杭州311300

出　　处：《通信学报》2022年第3期164-171,共8页Journal on Communications

基　　金：国家自然科学基金资助项目(No.U1809208);浙江省自然科学基金资助项目(No.LGG22F020010)。

摘　　要：为了充分利用评分的有效信息,并进一步研究评论的重要性,提出了一种融合评论文本特征和评分图卷积表示的推荐模型,利用图卷积编码学习用户和商品在评分上的特征表示,结合文本卷积特征,使用注意力机制来区分评论的重要性,然后通过隐因子模型把在评论和评分上学习到的特征表示融合产生推荐。在亚马逊公开数据集上的实验结果表明,提出的模型显著优于现有的模型,证明了提出的模型的有效性。In order to fully exploit the effective information of the ratings and further investigate the importance of the review,a recommendation model combining review’s feature and rating graph convolutional representation was proposed.Graph convolutional neural network was used to learn the representation of user and item from the ratings data.Combin-ing with text convolutional features,attention mechanism was utilized to distinguish the importance of the review.Finally,the representation learned from the review and the rating data was fused by the hidden factor model.The experimental results on Amazon’s public data showed that the proposed model significantly outperformed the traditional approaches,proving the effectiveness of the proposed model.

关 键 词：推荐模型 图卷积编码 注意力机制 隐因子模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]