数字图书馆中的关联书目检索推荐方法改进与设计
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摘要
针对数字图书馆中书目资源规模的增大导致对关联图书书目检索的时效性和准确性不好的问题,提出一种基于相似度标签索引和关联规则挖掘的数字图书馆中的关联书目检索推荐方法。计算数字图书馆中的关联图书书目的相似度标签信息参量,在相似度便签索引下进行图书检索的语义分析,在语义本体模型中通过关联规则挖掘实现对相似用户和相似书目的信息融合和协同推荐,提高了对数字图书馆的检索效能。仿真测试结果表明,该推荐方法相比于传统方法具有较高的推荐准确性。
Aiming at poor timeliness and low accuracy of association books bibliography retrieval caused by the increase of bibliographic resources in digital library,a recommendation retrieval method of association bibliographic in the digital library is put forward,which is based on similarity label index and association rules mining. The similarity label information parameters of correlation book bibliography in the digital library are calculated. Semantic analysis of book retrieval is conducted in combination with the similarity label index. The association rules mining is used to realize information fusion and collaborative recommendation of similar users and similar bibliography in the semantic ontology model,and improve the retrieval efficiency of digital library. The simulation test result show that the recommended method has higher accuracy,compared with the traditional methods.
Aiming at poor timeliness and low accuracy of association books bibliography retrieval caused by the increase of bibliographic resources in digital library,a recommendation retrieval method of association bibliographic in the digital library is put forward,which is based on similarity label index and association rules mining. The similarity label information parameters of correlation book bibliography in the digital library are calculated. Semantic analysis of book retrieval is conducted in combination with the similarity label index. The association rules mining is used to realize information fusion and collaborative recommendation of similar users and similar bibliography in the semantic ontology model,and improve the retrieval efficiency of digital library. The simulation test result show that the recommended method has higher accuracy,compared with the traditional methods.
引文
[1]王翠萍,杨冬梅.知识门户的个性化服务现状及优化研究[J].中国图书馆学报,2009,35(5):117-122.
[2]MAHMOUD E E.Complex complete synchronization of two non-identical hyperchaotic complex nonlinear systems[J].Mathemat-ical methods in the applied sciences,2014,37(3):321-328.
[3]PALOMARES I,MARTINEZ L,HERRERA F.A consensusmodel to detect and manage non-cooperative behaviors in largescale group decision making[J].IEEE trans on fuzzy system,2014,22(3):516-530.
[4]ZHANG H,WANG Z,LIU D A.Comprehensive review of sta-bility analysis of continuous-time recurrent neural networks[J].IEEE trans on neural networks and learning systems,2014,25(7):1229-1262.
[5]CRESPOA Rubén González,MARTíNEZB Oscar Sanjuán,LOVELLEB Juan Manuel Cueva,et al.Recommendation sys-tem based on user interaction data applied to intelligent elec-tronic books[J].Computers in human behavior,2011,27(4):1445-1449.
[6]AHU Sieg,BAMSHAD Mobasher,ROBIN Burke.Improvingthe effectiveness of collaborative recommendation with ontologybased user profiles[C]//Proceedings of the 1st InternationalWorkshop on Information Heterogeneity and Fusion in Recommender Systems.[S.l.]:Het Rec,2010:39-46.
[2]MAHMOUD E E.Complex complete synchronization of two non-identical hyperchaotic complex nonlinear systems[J].Mathemat-ical methods in the applied sciences,2014,37(3):321-328.
[3]PALOMARES I,MARTINEZ L,HERRERA F.A consensusmodel to detect and manage non-cooperative behaviors in largescale group decision making[J].IEEE trans on fuzzy system,2014,22(3):516-530.
[4]ZHANG H,WANG Z,LIU D A.Comprehensive review of sta-bility analysis of continuous-time recurrent neural networks[J].IEEE trans on neural networks and learning systems,2014,25(7):1229-1262.
[5]CRESPOA Rubén González,MARTíNEZB Oscar Sanjuán,LOVELLEB Juan Manuel Cueva,et al.Recommendation sys-tem based on user interaction data applied to intelligent elec-tronic books[J].Computers in human behavior,2011,27(4):1445-1449.
[6]AHU Sieg,BAMSHAD Mobasher,ROBIN Burke.Improvingthe effectiveness of collaborative recommendation with ontologybased user profiles[C]//Proceedings of the 1st InternationalWorkshop on Information Heterogeneity and Fusion in Recommender Systems.[S.l.]:Het Rec,2010:39-46.