《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (5): 12-22.doi: 10.6040/j.issn.1671-9352.7.2023.0001
范敏1,2,秦琴1,2,李金海1,2*
FAN Min1,2, QIN Qin1,2, LI Jinhai1,2*
摘要: 本文将三支决策思想、因果力理论与形式概念分析相结合,提出了三支因果力下的邻域推荐算法。考虑到极端用户评分对推荐精度的影响,根据宽松度和严苛度对用户进行分类,修正极端用户评分。基于修正评分矩阵计算节点之间的三支余弦相似度和节点相似结构重要度,找出专家节点。在对象弱概念需要满足的目标函数和约束条件下进行聚类得到邻域,在邻域中根据属性密度识别关键的条件属性和决策属性并计算置信度,结合三支因果力提取推荐规则对社区成员进行邻域推荐。实验结果表明,本文算法的精确度、召回率、F1均优于其他传统的推荐算法。
中图分类号:
[1] WILLE R. Restructuring lattice theory: an approach based on hierarchies of concepts[C] // Proceedings of the NATO Advanced Study Institute. Banff: Reidel, 1982:445-470. [2] GANTER B, WILLE R. Formal concept analysis: mathematical foundations[M]. New York: Springer, 1999. [3] 马娜,范敏,李金海. 复杂网络下的概念认知学习[J]. 南京大学学报(自然科学版), 2019, 55(4):609-623. MA Na, FAN Min, LI Jinhai. Concept-cognitive learning under complex network[J]. Journal of Nanjing University(Natural Sciences), 2019, 55(4):609-623. [4] 刘文星,范敏,李金海. 网络形式背景下的社区划分方法研究[J]. 计算机科学与探索, 2021, 15(8):1441-1449. LIU Wenxing, FAN Min, LI Jinhai. Research on community division method under network formal context[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8):441-1449. [5] QI Jianjun, WEI Ling, YAO Yiyu. Three-way formal concept analysis[C] //Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology. Shanghai: Springer, 2014:732-741. [6] QI Jianjun, QIAN Ting, WEI Ling. The connections between three-way and classical concept lattices[J]. Knowledge-based Systems, 2016, 91:143-151. [7] REN Ruisi, WEI Ling. The attribute reductions of three-way concept lattices[J]. Knowledge-Based Systems, 2016, 99:92-102. [8] LI Jinhai, HUANG Chenchen, QI Jianjun, et al. Three-way cognitive concept learning via multi-granularity[J]. Information Sciences, 2017, 378:244-263. [9] HUANG Chenchen, LI Jinhai, MEI Changlin, et al. Three-way concept learning based on cognitive operators: an information fusion viewpoint[J]. International Journal of Approximate Reasoning, 2017, 83:218-242. [10] SINGH P K. Three-way fuzzy concept lattice representation using neutrosophic set[J]. International Journal of Machine Learning and Cybernetics, 2017, 8(1):69-79. [11] YAO Yiyu. Interval sets and three-way concept analysis in incomplete contexts[J]. International Journal of Machine Learning and Cybernetics, 2017, 8(1):3-20. [12] 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[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(11):977-988. [13] KUO R J, LI S S. Applying particle swarm optimization algorithm-based collaborative filtering recommender system considering rating and review[J]. Applied Soft Computing, 2023, 135:110038. [14] ALHARBE N, RAKROUKI M A, ALJOHANI A. A collaborative filtering recommendation algorithm based on embedding representation[J]. Expert Systems with Applications, 2023, 215:119380. [15] XUE Feng, HE Xiangnan, WANG Xiang, et al. Deep item-based collaborative filtering for top-n recommendation[J]. ACM Transactions on Information Systems, 2019, 37(3):1-25. [16] LIAO M Q, SUNDAR S S. When e-commerce personalization systems show and tell: investigating the relative persuasive appeal of content-based versus collaborative filtering[J]. Journal of Advertising, 2022, 51(2):256-267. [17] SAHU S, KUMAR R, PATHAN M S, et al. Movie popularity and target audience prediction using the content-based recommender system[J]. IEEE Access, 2022, 10:42044-42060. [18] DE CAMPOS L M, FERNÁNDEZ-LUNA J M, HUETE J F, et al. Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks[J]. International Journal of Approximate Reasoning, 2010, 51(7): 785-799. [19] TIAN Yonghong, ZHENG Bing, WANG Yanfang, et al. College library personalized recommendation system based on hybrid recommendation algorithm[J]. Procedia CIRP, 2019, 83:490-494. [20] WALEK B, FAJMON P. A hybrid recommender system for an online store using a fuzzy expert system[J]. Expert Systems with Applications, 2023, 212:118565. [21] SOJAHROOD Z B, TALEAI M, CHENG H. Hybrid POI group recommender system based on group type in LBSN[J]. Expert Systems with Applications, 2023, 219:119681. [22] 张玉洁,杜雨露,孟祥武. 组推荐系统及其应用研究[J]. 计算机学报, 2016, 39(4): 745-764. ZHANG Yujie, DU Yulu, MENG Xiangwu. Research on group recommender systems and their applications[J].Chinese Journal of Computers, 2016, 39(4):745-764. [23] LEKAKOS G, GIAGLIS G M. Improving the prediction accuracy of recommendation algorithms: approaches anchored on human factors[J]. Interacting with Computers, 2006, 18(3):410-431. [24] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C] // Proceeding of the 10th International Conference on World Wide Web. New York: ACM, 2001:285-295. [25] ZAIER Z, GODIN R, FAUCHER L. Recommendation quality evolution based on neighborhood size[C] //Third International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution. Barcelona: IEEE, 2007:33-36. [26] SINGH P K, SINHA M, DAS S, et al. Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item[J]. Applied Intelligence, 2020, 50:4708-4731. [27] YAO Yiyu. Three-way decisions with probabilistic rough sets[J]. Information Sciences, 2010, 180(3):341-353. [28] LONG Binghan, XU Weihua, ZHANG Xiaoyan, et al. The dynamic update method of attribute-induced three-way granular concept in formal contexts[J]. International Journal of Approximate Reasoning, 2020, 126:228-248. [29] 徐伟华,杨蕾,张晓燕. 模糊三支形式概念分析与概念认知学习[J]. 西北大学学报(自然科学版), 2020, 50(4):516-528. XU Weihua, YANG Lei, ZHANG Xiaoyan. Fuzzy three-way formal concept analysis and concept-cognitive learning[J]. Journal of Northwest University(Natural Science Edition), 2020, 50(4):516-528. [30] 徐伟华,林玉飞. 模糊三支概念簇的理论与实践[J]. 重庆邮电大学学报(自然科学版), 2023, 35(1):40-48. XU Weihua, LIN Yufei. Theory and practice of fuzzy three-way concept cluster[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2023, 35(1):40-48. [31] ZHANG Hengru, MIN Fan. Three-way recommender systems based on random forests[J]. Knowledge-based Systems, 2016, 91:275-286. [32] 叶晓庆,刘盾,梁德翠. 基于协同过滤的三支粒推荐算法研究[J]. 计算机科学, 2018, 45(1):90-96. YE Xiaoqing, LIU Dun, LIANG Decui. Three-way granular recommendation algorithm based on collaborative filtering[J]. Computer Science, 2018, 45(1):90-96. [33] LIU Dun, YE Xiaoqing. A matrix factorization based dynamic granularity recommendation with three-way decisions[J]. Knowledge-based Systems, 2020, 191:105243. [34] XIE Xiaoliang, HE Jiali, HUANG Dan. Three-way group decision in a covering decision information system[J]. IEEE Access, 2020, 8:5172-5188. [35] 李娴,张泽华,赵霞,等. TWD-GNN: 基于三支决策的图神经网络推荐方法[J]. 计算机工程与应用, 2020, 56(12):156-162. LI Xian, ZHANG Zehua, ZHAO Xia, et al. TWD-GNN: recommendation method of graph neural network based on three-way decision[J]. Computer Engineering and Application, 2020, 56(12):156-162. [36] PEAR L J. Causality[M]. Cambridge: Cambridge University Press, 2009. [37] MARTÍNEZ-SÁNCHEZ J F, VENEGAS-MARTÍNEZ F, PÉREZ-LECHUGA G. Money laundering risk management in multiple-purpose financial institutions in Mexico: a Bayesian network approach[J]. Journal of Money Laundering Control, 2023, 26(4):845-861. [38] AHIR H, BLOOM N, FURCERI D. The world uncertainty index[R]. Massachusetts, America: National Bureau of Economic Research, 2022. [39] FAN Y, CHEN J Q, SHIRKEY G, et al. Applications of structural equation modeling(SEM)in ecological studies: an updated review[J]. Ecological Processes, 2016, 5(19):1-12. [40] NADATHUR P, LAUER S. Causal necessity, causal sufficiency, and the implications of causativeverbs[J]. Glossa: A Journal of General Linguistics, 2020, 5(1):1-37. [41] GUO Ruocheng, CHENG Lu, LI Jundong, et al. A survey of learning causality with data: problems and methods[J]. ACM Computing Surveys, 2020, 53(4):1-37. [42] LEE A, INCEOGLU I, HAUSER O, et al. Determining causal relationships in leadership research using machine learning: the powerful synergy of experiments and datascience[J]. The Leadership Quarterly, 2022, 33(5):101426. [43] FAN Min, LUO Shan, LI Jinhai. Network rule extraction under the network formal context based on three-way decision[J]. Applied Intelligence, 2023, 53(5):5126-5145. [44] 刘忠慧,李鑫,闵帆. 内涵粗糙三支概念及个性化推荐[J]. 西北大学学报(自然科学版), 2022, 52(5):774-783. LIU Zhonghui, LI Xin, MIN Fan. Three-way concept with rough intent and its application in personalized recommendation[J]. Journal of Northwest University(Natural Science Edition), 2022, 52(5):774-783. |
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