Recommended Model for Fusing Multi-Source Heterogeneous Data Based on Deep Learning

被引：0
作者：
Ji Z.-Y. [1 ]
Song X.-J. [1 ]
Pi H.-Y. [1 ]
Yang C. [1 ]
机构：
[1] School of Software Engineering, Beijing Jiaotong University, Beijing
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2019年 / 42卷 / 06期
关键词：
Deep learning; Multi-source heterogeneous data; Recommendation model; Social network;
D O I：
10.13190/j.jbupt.2019-164
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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页码：35 / 42
页数：7
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