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| 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 |
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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.
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Received: 18 October 2022
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| Fund:National Natural Science Foundation of China(No.11971211,12171388) |
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Corresponding Authors:
LI Jinhai, Ph.D., professor. His research interests include cognitive computing, granular computing, big data analysis, concept lattice and rough set.
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| About author:: FAN Min, Ph.D., associate professor. Her research interests include data mining, rough set, granular computing and social network analysis. GUO Ruixin, master student. Her research interests include data mining and social network analysis. |
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2022, Vol. 35 
