Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 78-84.doi: 10.11896/jsjkx.200400023

• Database & Big Data & Data Science • Previous Articles     Next Articles

Recommendation Algorithm Based on Bipartite Graph Convolution Representation

XIONG Xu-dong1, DU Sheng-dong1,2,3, XIA Wan-jun1, LI Tian-rui1,2,3   

  1. 1 School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China
    2 Institute of Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 611756,China
  • Received:2020-06-24 Revised:2020-09-20 Online:2021-04-15 Published:2021-04-09
  • About author:XIONG Xu-dong,born in 1995,postgraduate.His main research interests include recommendation algorithms and network representation learning.(asia123dong@126.com)
    LI Tian-rui,born in 1969,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include big data intelligence,rough sets and granular computing.
  • Supported by:
    National Key R&D Program of China(2017YFB1401400).

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Abstract: With the rapid development of data-driven intelligent technology,personalized intelligent recommendation algorithms and related applications become research hotspots.Recommendation can be regarded as a matching problem between users and items.As the semantic gap between users and items,it’s inconvenient to match them directly.Many existing recommendation methods based on deep learning use the idea of mapping entities from different spaces into a unified semantic space to calculate the matching degree by embedding representation.With the emergence of network representation learning,the bipartite graph can be formed between users and items and the embedding representation in the recommendation algorithm can also be regarded as nodes representation in bipartite graph.Many recommendation algorithms based on bipartite graph nodes representation have been proposed.However,existing algorithms are still hard to extract high-order interactive information effectively.To solve this problem,a bipartite graph convolution representation-based recommendation algorithm(BGCRRA) is developed in this paper.Interactions between users and items are regarded as a bipartite graph at first,nodes are represented by adaptively fusing multi-order and multi-level graphs secondly,and finally the matching degree of users and items is calculated and the recommendation is realized.Comparative experiments are carried out on 3 open datasets and the effectiveness of the proposed algorithm is verified by comparing HR and NDCG(Normalized Discounted Cumulative Gain) indexes of our algorithm and the state-of-the-art algorithms.

Key words: Bipartite graph, Embedding methods, Graph convolution, Recommendation algorithms

CLC Number: 

  • TP391
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