Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6): 234-240.doi: 10.11896/jsjkx.200500136

• Artificial Intelligence • Previous Articles     Next Articles

Heuristic Construction of Triadic Concept and Its Application in Social Recommendation

LIU Zhong-hui1, ZHAO Qi1, ZOU Lu1, MIN Fan1,2   

  1. 1 School of Computer Science,Southwest Petroleum University,Chengdu 610500,China
    2 Institute for Artificial Intelligence,Southwest Petroleum University,Chengdu 610500,China
  • Received:2020-05-27 Revised:2020-08-17 Online:2021-06-15 Published:2021-06-03
  • About author:LIU Zhong-hui,born in 1980,postgra-duate,associate professor,master supervisor,is a member of China Compu-ter Federation.Her main research in-terests include machine learning,formal concept analysis and rough set.(Lz_hui@126.com)
    MIN Fan,born in 1973,Ph.D,professor,Ph.D supervisor,is a member of CAAI granule computing and knowledge discovery committee and China Computer Federaton.His main research interests include granular computing,recommender systems and active learning.
  • Supported by:
    National Natural Science Foundation of China(41604114) and Sichuan Province Youth Science and Technology Innovation Team(2019JDTD0017).

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Abstract: Formal concept analysis is a knowledge discovery method that has great achievements in theory and application.Recently,with the emergence of three-dimensional data,triadic formal concept analysis has been developed.However,there are few researches and applications in this field,especially it has not been applied to recommendation systems.This paper proposes an efficient triadic concept set construction method and applies it to social recommendation.Firstly,the heuristic information is designed to generate a set of triadic concepts covering all users.Heuristic information aims to construct strong concepts with a certain scale of extension and intension.Then,appropriate social relations are screened through the attributes of the proposed items,and the recommendation prediction is realized by combining the popularity of the items in the concept.Three experiments are carried out in real data set and sampled data set respectively.In the first experiment,the number of triadic concepts and running time constructed by the heuristic method and ∨oc operation are compared respectively.The concepts constructed by the ∨oc operation do not significantly improve the recommendation effect.The second experiment compares the accuracy,recall rate and F1 of the recommendation effect.It reveals that increasing the number of conditions can effectively improve the recommendation effect.The results of the last experiment show that the recommendation effect of the new algorithm is better than that of KNN and GRHC.

Key words: Heuristic algorithm, Item conditions, Popularity, Social recommendation, Triadic formal concept analysis

CLC Number: 

  • TP181
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