基于模拟退火法的概念集构造算法

doi:10.16451/j.cnki.issn1003-6059.202108005

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Abstract In formal concept analysis, the construction of concept lattice produces high time and space complexity, but only partial lattices or concept sets are applied in recommendation. To solve this problem, a construction algorithm of concept set based on simulated annealing algorithm is proposed. The candidate concepts generation technique is presented based on the simulated annealing algorithm. The objective function takes the extension similarity of a concept into account. The Metropolis criterion is employed to update the solution. The concept filtering technique is designed based on all candidate concepts. Strong concepts of each user are selected with the extension similarity as the evaluation indicator, and the filtered strong concepts constitute a concept set. The recommendation technique is proposed based on the strong concept set. It provides personalized recommendations to the target user using the preferences of neighbor users in the same extension. Experimental results on 5 public datasets demonstrate that the recommendation performance and the efficiency of proposed algorithm are superior.

Key words： Formal Concept Analysis Simulated Annealing Algorithm Concept Set Extension Similarity Personalized Recommendation

Received: 07 May 2021

ZTFLH:

TP 181

Fund:National Natural Science Foundation of China(No.41674141)

Corresponding Authors: MIN Fan, Ph.D., professor. His research interests include gra-nular computing, recommender system and active learning.

About author:: LIU Zhonghui, master, associate profe-ssor. Her research interests include machine learning, formal concept analysis and rough set.CHEN Jianyu, master student. His research interests include formal concept analysis and recommender system.SONG Guojie, Ph.D., professor. His research interests include deep learning and high performance computing.

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Cite this article:

LIU Zhonghui,CHEN Jianyu,SONG Guojie等. Construction Algorithm of Concept Set Based on Simulated Annealing Algorithm[J]. , 2021, 34(8): 723-732.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202108005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I8/723

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