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.
Fund:National Natural Science Foundation of China(No.11971211,12171388)
Corresponding Authors:
LI Jinhai, Ph.D., professor. His research interests include cognitive computing, granular computing, big data analysis, concept lattice and rough set.
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.
FAN Min,GUO Ruixin,LI Jinhai. Neighborhood Recommendation Algorithm Based on Causality Force under Network Formal Decision Context[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(11): 977-988.
[1] WILLE R.Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts // RIVAL I, ed. Ordered Sets. Berlin, Germany: Springer, 1981: 445-470. [2] 许佳卿,彭鑫,赵文耘.一种基于模糊概念格和代码分析的软件演化分析方法.计算机学报, 2009, 32(9): 1832-1844. (XU J Q, PENG X, ZHAO W Y.An Evolution Analysis Method Based on Fuzzy Concept Lattice and Source Code Analysis. Chinese Journal of Computers, 2009, 32(9): 1832-1844.) [3] ROCCO C M, HERNANDEZ-PERDOMO E, MUN J.Introduction to Formal Concept Analysis and Its Applications in Reliability Engineering. Reliability Engineering and System Safety, 2020, 202. DOI: 10.1016/j.ress.2020.107002. [4] POELMANS J, IGNATOV D I, KUZNETSOV S O, et al. Formal Concept Analysis in Knowledge Processing: A Survey on Applications. Expert Systems with Applications, 2013, 40(16): 6538-6560. [5] ZOU L, LIN H M, SONG X Y, et al. Rule Extraction Based on Linguistic-Valued Intuitionistic Fuzzy Layered Concept Lattice. International Journal of Approximate Reasoning, 2021, 133: 1-16. [6] KAYTOUE M, KUZNETSOV S O, NAPOLI A, et al. Mining Gene Expression Data with Pattern Structures in Formal Concept Analysis. Information Sciences, 2011, 181(10): 1989-2001. [7] 陈昊文,王黎明,张卓.基于概念邻域的Top-N推荐算法.小型微型计算机系统, 2017, 38(11): 2553-2559. (CHEN H W, WANG L M, ZHANG Z.Top-N Recommendation Algorithm Based on Conceptual Neighborhood. Journal of Chinese Computer Systems, 2017, 38(11): 2553-2559.) [8] 米允龙,李金海,刘文奇,等.MapReduce框架下的粒概念认知学习系统研究.电子学报, 2018, 46(2): 289-297. (MI Y L, LI J H, LIU W Q, et al. Research on Granular Concept Cognitive Learning System under MapReduce Framework. Acta Electronica Sinica, 2018, 46(2): 289-297.) [9] 马娜,范敏,李金海.复杂网络下的概念认知学习.南京大学学报(自然科学版), 2019, 55(4): 609-623. (MA N, FAN M, LI J H.Concept-Cognitive Learning under Complex Network. Journal of Nanjing University(Natural Science), 2019, 55(4): 609-623.) [10] KUMAR C A, ISHWARYA M S, LOO C K.Formal Concept Analysis Approach to Cognitive Functionalities of Bidirectional Associative Memory. Biologically Inspired Cognitive Architectures, 2015, 12: 20-33. [11] YAO Y Y.Concept Lattices in Rough Set Theory // Proc of the IEEE Annual Meeting of the Fuzzy Information. Washington, USA: IEEE, 2004: 796-801. [12] DÜNTSCH I, GEDIGA G. Modal-Style Operators in Qualitative Data Analysis // Proc of the IEEE International Conference on Data Mining. Washington, USA: IEEE, 2002: 155-162. [13] YAO Y Y.A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis // Proc of the 4th International Conference on Rough Sets and Current Trends in Computing. Berlin, Germany: Springer, 2004: 59-68. [14] 闫梦宇,李金海.概念格共有与独有属性(对象)的关系研究.计算机科学与探索, 2019, 13(4): 702-710. (YAN M Y, LI J H.Research on Relationship Between Common and Unique Attributes(Objects) of Concept Lattice. Journal of Frontiers of Computer Science and Technology, 2019, 13(4): 702-710.) [15] ZAIER Z, GODIN R, FAUCHER L.Recommendation Quality Evolution Based on Neighborhood Size // Proc of the 3rd International Conference on Automated Production of Cross Media Content for Multi-channel Distribution. Washington, USA: IEEE, 2007: 33-36. [16] GARCIA I, PAJARES S, SEBASTIA L, et al. Preference Elicitation Techniques for Group Recommender Systems. Information Sciences, 2012, 189: 155-175. [17] YI J, ZHANG L, PHELAN C A.A Novel Recommendation Stra-tegy for User-Based Collaborative Filtering in Intelligent Marketing. Journal of Digital Information Management, 2016, 14(2): 81-91. [18] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-Based Co-llaborative Filtering Recommendation Algorithms // Proc of the 10th International Conference on World Wide Web. New York, USA: ACM, 2001: 285-295. [19] 张玉洁,杜雨露,孟祥武.组推荐系统及其应用研究.计算机学报, 2016, 39(4): 745-764. (ZHANG Y J, DU Y L, MENG X W.Research on Group Recommender Systems and Their Applications. Chinese Journal of Computers, 2016, 39(4): 745-764.) [20] PEARL J. Causality. 2nd Edition. Cambridge, UK: Cambridge University Press, 2009. [21] FAN Y, CHEN J Q, SHIRKEY G, et al. Applications of Structu-ral Equation Modeling(SEM) in Ecological Studies: An Updated Review. Ecological Processes, 2016, 5. DOI: 10.1186/s13717-016-0063-3. [22] MARIA-YANELI A A, NICANDRO C R, EFRÉN M M, et al. Assessment of Bayesian Network Classifiers as Tools for Discriminating Breast Cancer Pre-diagnosis Based on Three Diagnostic Methods // Proc of the 11th Mexican International Conference on Artificial Intelligence. Berlin, Germany: Springer, 2012: 419-431. [23] WOLFF P.Direct Causation in the Linguistic Coding and Individuation of Causal Events. Cognition, 2003, 88(1): 1-48. [24] NADATHUR P, LAUER S. Causal Necessity, Causal Sufficiency, and the Implications of Causative Verbs. Glossa: A Journal of General Linguistics, 2020, 5(1). DOI: 10.5334/GJGL.497. [25] SLOMAN S, BARBEY A K, HOTALING J M.A Causal Model Theory of the Meaning of Cause, Enable, and Prevent. Cognitive Science, 2009, 33(1): 21-50. [26] GEFFNER H, DECHTER R, HALPERN J Y.Probabilistic and Causal Inference: The Works of Judea Pearl. New York, USA: ACM, 2022. [27] HOEL E P, ALBANTAKIS L, TONONI G.Quantifying Causal Emergence Shows that Macro Can Beat Micro. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(49): 19790-19795. [28] COMOLATTI R, HOEL E.Causal Emergence Is Widespread Across Measures of Causation[C/OL]. [2022-09-03].https://arxiv.org/pdf/2202.01854.pdf. [29] EELLS E. Probabilistic Causality. Cambridge, UK: Cambridge Uni-versity Press, 2008. [30] CHENG P W, NOVICK L R.Causes Versus Enabling Conditions. Cognition, 1991, 40(1/2): 83-120. [31] LEWIS D.Causation. Journal of Philosophy, 1973, 70: 556-567. [32] 刘忠慧,邹璐,杨梅,等.启发式概念构造的组推荐方法.计算机科学与探索, 2020, 14(4): 703-711. (LIU Z H, ZOU L, YANG M, et al. Group Recommendation with Concept of Heuristic Construction. Journal of Frontiers of Computer Science and Technology, 2020, 14(4): 703-711.) [33] MAIO C D, FENZA G, GAETA M, et al. RSS-Based E-Learning Recommendations Exploiting Fuzzy FCA for Knowledge Modeling. Applied Soft Computing, 2012, 12(1): 113-124. [34] 刘忠慧,陈建宇,宋国杰,等.基于模拟退火法的概念集构造算法.模式识别与人工智能, 2021, 34(8): 723-732. (LIU Z H, CHEN J Y, SONG G J, et al. Construction Algorithm of Concept Set Based on Simulated Annealing Algorithm. Pattern Recognition and Artificial Intelligence, 2021, 34(8): 723-732.) [35] ZOU C F, ZHANG D Q, WAN J F, et al. Using Concept Lattice for Personalized Recommendation System Design. IEEE Systems Journal, 2017, 11(1): 305-314. [36] DU BOUCHER-RYAN P, BRIDGE D. Collaborative Recommending Using Formal Concept Analysis. Knowledge-Based Systems, 2006, 19(5): 309-315. [37] GODIN R, MISSAOUI R, ALAOUI H.Incremental Concept Formation Algorithms Based on Galois(Concept) Lattices. Computa-tional Intelligence, 1995, 11(2): 246-267. [38] DJOUADI Y, PRADE H.Interval-Valued Fuzzy Galois Connections: Algebraic Requirements and Concept Lattice Construction. Fundamenta Informaticae, 2010, 99(2): 169-186. [39] ALBANTAKIS L, MARSHALL W, HOEL E, et al. What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks. Entropy(Basel), 2019, 21(5). DOI: 10.3390/e21050459. [40] 范敏,张洁,李金海.基于弱概念相似度的组推荐方法[J/OL]. [2022-09-03].https://www.researchgate.net/publication/366874633_Group_Recommendation_Method_Based_on_Weaken-concept_Similarity. (FAN M, ZHANG J, LI J H.Group Recommendation Method Based on Weaken-Concept Similarity[J/OL]. [2022-09-03].https://www.researchgate.net/publication/366874633_Group_Recommendation_Method_Based_on_Weaken-concept_Similarity.) [41] FAN W Q, MA Y, LI Q, et al. Graph Neural Networks for Social Recommendation // Proc of the World Wide Web Conference. New York, USA: ACM, 2019: 417-426. [42] DUAN Z C, XU W H, CHEN Y T, et al. ETBRec: A Novel Re-commendation Algorithm Combining the Double Influence of Trust Relationship and Expert Users. Applied Intelligence, 2022, 52(1): 282-294. [43] MA H F, JIA M H Z, ZHANG D, et al. Combining Tag Correlation. Combining Tag Correlation and User Social Relation for Microblog Recommendation. Information Sciences, 2017, 385/386: 325-337.
2022, Vol. 35 
