Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 397-401.

• Big Data & Data Mining • Previous Articles     Next Articles

MetaStruct-CF:A Meta Structure Based Collaborative Filtering Algorithm in Heterogeneous Information Networks

WANG Xu1, PANG Wei2, WANG Zhe1   

  1. College of Computer Science and Technology,Jilin University,Changchun 130012,China1;
    School of Natural and Computing Sciences,University of Aberdeen,Aberdeen AB253TN,United Kingdom2
  • Online:2019-06-14 Published:2019-07-02

Abstract: In recent years,heterogeneous information networks (HINs) have received a lot of attention as they contain rich semantic information.Previous works have demonstrated that the rich relationship information in HINs can effectively improve the recommendation performance.As an important tool for mining relationship information in HINs,meta-path has been widely used in many algorithms.However,because of its simple linear structure,meta-path may not be able to express complex relationship information.To address this issue,this paper proposed a new recommendation algorithm,Metastruct-CF,which applies Meta structure to capture the accurate relationship information among data objects.Different from existing methods,the proposed combines algorithm multiple relationships to effectively utilize the information in HINs.Extensive experiments on two real world datasets show that this algorithm achieves better recommendation performance than several popular or state-of-the-art methods.

Key words: Collaborative filtering, Heterogeneous information network, Meta structure, Recommendation systems

CLC Number: 

  • TP391
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