基于深度学习的融合多源异构数据的推荐模型

doi:10.13190/j.jbupt.2019-164

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (6): 35-42.doi: 10.13190/j.jbupt.2019-164

基于深度学习的融合多源异构数据的推荐模型

冀振燕, 宋晓军, 皮怀雨, 杨春

北京交通大学软件学院, 北京 100044

收稿日期:2019-07-30 出版日期:2019-12-28 发布日期:2019-11-15
作者简介:冀振燕(1970-),女,副教授,硕士生导师,E-mail:jzhenyan@hotmail.com.
基金资助:
国家自然科学基金重点项目（S19A200010）；国家重点研发计划项目（R19B5200010）

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

JI Zhen-yan, SONG Xiao-jun, PI Huai-yu, YANG Chun

School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China

Received:2019-07-30 Online:2019-12-28 Published:2019-11-15
Supported by:

摘要/Abstract

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.

关键词: 多源异构数据, 深度学习, 推荐模型, 社交网络

Abstract: 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.

Key words: multi-source heterogeneous data, deep learning, recommendation model, social network

中图分类号:

TP311

冀振燕, 宋晓军, 皮怀雨, 杨春. 基于深度学习的融合多源异构数据的推荐模型[J]. 北京邮电大学学报, 2019, 42(6): 35-42.

JI Zhen-yan, SONG Xiao-jun, PI Huai-yu, YANG Chun. Recommended Model for Fusing Multi-Source Heterogeneous Data Based on Deep Learning[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 35-42.

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基于深度学习的融合多源异构数据的推荐模型

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

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