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

doi:10.13190/j.jbupt.2019-164

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (6): 35-42.doi: 10.13190/j.jbupt.2019-164

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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:

Abstract

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

CLC Number:

TP311

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

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