个性化旅游景点推荐中考虑约束的关联规则挖掘算法
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摘要
在约束子集定义的基础上,提出面向旅游景点推荐的约束关联规则挖掘算法,将最为耗时的目标项集搜索限定在约束子集中,降低了数据集搜索的规模.同时通过约束条件提升了最终规则生成的针对性,避免大量无趣规则的生成,使得挖掘算法效率更高、挖掘结果更符合用户需求.最后在望路者文化旅游服务数据集中开展了示范应用研究,验证了所提出的算法可为旅游景点推荐提供更为合理的信息.
This paper proposes a constrained association rule mining algorithm, called ConstrainARM, for the recommendation of tourist spots on the basis of the definition of constrained sub-itemsets. The algorithm confines the compute-intensive search of target itemsets on the traversal of the constrained sub-itemsets, thus, the algorithm's efficiency is improved to some extent by the reducing of the scale of the traversal data set, meanwhile, the constraints are used to improve the pertinence of rule generation that is oriented to the user's requirements, hence, the frequent itemsets based rule generation can avoided effectively a large number of rules with lower interest. Consequently, the algorithm proposed is more efficient, and the mining results are more in line with the needs of users. Finally, the demonstration and application of the ConstrainARM algorithm is carried out in the tourist data of Wangluzhe cultural tourism service network operation platform(WCTSNOP), and it is verified that the proposed algorithm can provide more reasonable and useful information for the recommendation of tourist spots.
This paper proposes a constrained association rule mining algorithm, called ConstrainARM, for the recommendation of tourist spots on the basis of the definition of constrained sub-itemsets. The algorithm confines the compute-intensive search of target itemsets on the traversal of the constrained sub-itemsets, thus, the algorithm's efficiency is improved to some extent by the reducing of the scale of the traversal data set, meanwhile, the constraints are used to improve the pertinence of rule generation that is oriented to the user's requirements, hence, the frequent itemsets based rule generation can avoided effectively a large number of rules with lower interest. Consequently, the algorithm proposed is more efficient, and the mining results are more in line with the needs of users. Finally, the demonstration and application of the ConstrainARM algorithm is carried out in the tourist data of Wangluzhe cultural tourism service network operation platform(WCTSNOP), and it is verified that the proposed algorithm can provide more reasonable and useful information for the recommendation of tourist spots.
引文
[1] FANG H,CHEN C,LIN J,et al.Association rule analysis for tour route recommendation and application to WCTSNOP[C]//International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences.Wuhan:[s.n.],2017:1121-1126.
[2] MARTíN D,MARTíNEZ-BALLESTEROS M,GARCíA-GIL D,et al.Mrqar:a generic mapreduce framework to discover quantitative association rules in big data problems[J].Knowledge-Based Systems,2018,153:176-192.
[3] NGUYEN D,VO B,LE B.Ccar:an efficient method for mining class association rules with itemset constraints[J].Engineering Applications of Artificial Intelligence,2015,37:115-124.
[4] NGUYEN D,NGUYEN L T T,VO B,et al.Efficient mining of class association rules with the itemset constraint[J].Knowledge-Based Systems,2016,103(C):73-88.
[5] KAUR A,AGGARWAL V,SHANKAR S K.An efficient algorithm for generating association rules by using constrained itemsets mining[C]//2016 IEEE International Conference on Recent Trends in Electronics,Information and Communication Technology.Bengaluru:IEEE,2016:99-102.
[6] 郭文月,刘海砚,余岸竹,等.非指定时间约束的社会安全事件关联规则挖掘[J].地理与地理信息科学,2016,32(3):14-18.
[7] KARLSSON I,PAPAPETROU P,ASKER L.Kapminer:mining ordered association rules with constraints[C]// 16th International Symposium on Intelligent Data Analysis.London:Springer,2017:149-161.
[8] NGUYEN L T T,BAY Y,SON N H,et al.Mining class association rules with synthesis constraints[C]// 9th Asian Conference on Intelligent Information and Database Systems.Kanazawa:Springer,2017:556-565.
[9] 何占军,邓敏,蔡建南,等.顾及背景知识的多事件序列关联规则挖掘方法[J].武汉大学学报(信息科学版),2018,43(5):766-772.
[10] VERSICHELE M,GROOTE L D,BOUUAERT M C,et al.Pattern mining in tourist attraction visits through association rule learning on bluetooth tracking data:a case study of Ghent,Belgium[J].Tourism Management,2014,44:67-81.
[11] TEYAKOME J,EIAMKANITCHAT N,SANGKAKORN K,et al.Fuzzy clustering and association rule for improvement of visitor spots in the Lanna[C]// 3rd International Conference on Information Science and Security.Pattaya:[s.n.],2016:161-164.
[12] 张久滕,吴小竹,陈崇成,等.基于时间框架的多日游行程规划及其优化方法[J].福州大学学报(自然科学版),2018,46(6):787-793.
[13] JOUN H.Association rule analysis and application of informal data on accommodations:demand and supply perspective in Irts 2008[J].Journal of Tourism Sciences,2018,42(5):137-150.
[2] MARTíN D,MARTíNEZ-BALLESTEROS M,GARCíA-GIL D,et al.Mrqar:a generic mapreduce framework to discover quantitative association rules in big data problems[J].Knowledge-Based Systems,2018,153:176-192.
[3] NGUYEN D,VO B,LE B.Ccar:an efficient method for mining class association rules with itemset constraints[J].Engineering Applications of Artificial Intelligence,2015,37:115-124.
[4] NGUYEN D,NGUYEN L T T,VO B,et al.Efficient mining of class association rules with the itemset constraint[J].Knowledge-Based Systems,2016,103(C):73-88.
[5] KAUR A,AGGARWAL V,SHANKAR S K.An efficient algorithm for generating association rules by using constrained itemsets mining[C]//2016 IEEE International Conference on Recent Trends in Electronics,Information and Communication Technology.Bengaluru:IEEE,2016:99-102.
[6] 郭文月,刘海砚,余岸竹,等.非指定时间约束的社会安全事件关联规则挖掘[J].地理与地理信息科学,2016,32(3):14-18.
[7] KARLSSON I,PAPAPETROU P,ASKER L.Kapminer:mining ordered association rules with constraints[C]// 16th International Symposium on Intelligent Data Analysis.London:Springer,2017:149-161.
[8] NGUYEN L T T,BAY Y,SON N H,et al.Mining class association rules with synthesis constraints[C]// 9th Asian Conference on Intelligent Information and Database Systems.Kanazawa:Springer,2017:556-565.
[9] 何占军,邓敏,蔡建南,等.顾及背景知识的多事件序列关联规则挖掘方法[J].武汉大学学报(信息科学版),2018,43(5):766-772.
[10] VERSICHELE M,GROOTE L D,BOUUAERT M C,et al.Pattern mining in tourist attraction visits through association rule learning on bluetooth tracking data:a case study of Ghent,Belgium[J].Tourism Management,2014,44:67-81.
[11] TEYAKOME J,EIAMKANITCHAT N,SANGKAKORN K,et al.Fuzzy clustering and association rule for improvement of visitor spots in the Lanna[C]// 3rd International Conference on Information Science and Security.Pattaya:[s.n.],2016:161-164.
[12] 张久滕,吴小竹,陈崇成,等.基于时间框架的多日游行程规划及其优化方法[J].福州大学学报(自然科学版),2018,46(6):787-793.
[13] JOUN H.Association rule analysis and application of informal data on accommodations:demand and supply perspective in Irts 2008[J].Journal of Tourism Sciences,2018,42(5):137-150.