Neighborhood Recommendation Algorithm Based on Causality Force under Network Formal Decision Context
FAN Min1,2, GUO Ruixin1,2, LI Jinhai1,2
1. Data Science Research Center, Kunming University of Science and Technology, Kunming 650500; 2. Faculty of Science, Kunming University of Science and Technology, Kunming 650500
Abstract：Concept cognition and knowledge discovery under network data are hot research directions in the field of network data analysis, and they are applied in the field of recommendation system. However, how to construct a reasonable set of weaken-concepts to improve the effectiveness of neighborhood recommendation is still a difficult problem. To solve this problem, a set of variable precision weaken-concepts is proposed to induce neighborhoods with more information, and then a neighborhood recommendation algorithm is developed based on causality force. Firstly, the aggregation centrality degree of similarity network is defined to determine expert nodes, and a set of variable precision weaken-concepts is obtained to divide neighborhoods. Secondly, the variable precision common operators are employed in each neighborhood to obtain the weaken-concepts of conditional attributes and decision attributes of objects. Finally, a neighborhood recommendation algorithm is given based on the principle of causality force and related properties. Experimental results on MovieLens and Filmtrust datasets show that the accuracy, recall, F1 and running time of the proposed algorithm are greatly improved.
范敏, 郭瑞欣, 李金海. 网络决策形式背景下基于因果力的邻域推荐算法[J]. 模式识别与人工智能, 2022, 35(11): 977-988.
FAN Min, GUO Ruixin, LI Jinhai. Neighborhood Recommendation Algorithm Based on Causality Force under Network Formal Decision Context. Pattern Recognition and Artificial Intelligence, 2022, 35(11): 977-988.
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