摘要
为了充分利用多源异构数据所提供的信息提高推荐准确度,提出一个基于深度学习的混合推荐模型.该模型融合评分、评论和社交网络数据进行推荐,采用深度学习方法对文本和评分进行特征学习,然后使用社交网络对采样进行约束,从而得到更准确的用户和物品的特征表示.实验结果表明,该方法具有较高的准确度.
Considering that Internet information today is diverse and inconsistent in structure,in order to fully utilize the information provided by multi-source heterogeneous data to improve the recommendation accuracy,a hybrid recommendation model based on deep learning was proposed.The model makes a recommendation based on combining ratings,review texts and social network data.The model also adopts deep learning to learn features of reviews and ratings,and then uses social network to constraint sampling.Experiments show that the model is of higher accurate feature representations of users and items.
作者
冀振燕
宋晓军
皮怀雨
杨春
JI Zhen-yan;SONG Xiao-jun;PI Huai-yu;YANG Chun(School of Software Engineering,Beijing Jiaotong University,Beijing 100044,China)
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2019年第6期35-42,共8页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金重点项目(S19A200010)
国家重点研发计划项目(R19B5200010).
关键词
多源异构数据
深度学习
推荐模型
社交网络
multi-source heterogeneous data
deep learning
recommendation model
social network
