Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 51-58.doi: 10.11896/jsjkx.200900194

• Artificial Intelligence • Previous Articles     Next Articles

Fusion Vectorized Representation Learning of Multi-source Heterogeneous User-generated Contents

JI Nan-xun, SUN Xiao-yan, LI Zhen-qi   

  1. School of Information and Control Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221008,China
  • Received:2020-09-27 Revised:2021-01-08 Online:2021-10-15 Published:2021-10-18
  • About author:JI Nan-xun,born in 1994,postgraduate.His main research interests include na-tural language processing and machine learning.
    SUN Xiao-yan,born in 1978,Ph.D professor.Her main research interests include interactive evolutionary computation,big data and intelligence optimization.
  • Supported by:
    National Natural Science Foundation of China(61876184).

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Abstract: With the development of mobile networks and APPs,user generated contents (UGC) containing multi-source heterogeneous data such as evaluations,markings,scoring,images and videos are greatly valuable information for improving the quality of personalized services.The representation learning of fusion and vectorization on the multi-source heterogeneous UGC is the most critical issue for the successful application.Motivated by this,we propose a representation learning method for effectively fusing and vectorizing the comments and image data.We utilize the Doc2vec and LDA models to sufficiently extract the features of the multi-source comments.The images correlated with the comments are represented with deep convolutional network.A hybrid vectorized representation learning for fusing comments and a convolution strategy for integrating images and comments are presented.The feasibility and effectiveness of the proposed method is demonstrated by applying it to typical Amazon public data sets with heterogeneous UGC,in which the vectorized multi-source heterogeneous UGC is taken as the representation of each product and the classification accuracy of the products are compared.

Key words: Fusion, Multi-source heterogeneous, Representation learning, Short text, User generated contents

CLC Number: 

  • TP391
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