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现代图书情报技术  2014, Vol. 30 Issue (4): 20-26    DOI: 10.11925/infotech.1003-3513.2014.04.04
  数字图书馆 本期目录 | 过刊浏览 | 高级检索 |
利用演化模式做文献推荐
尉萌1,2
1. 武汉大学信息管理学院 武汉 430072;
2. 空军工程大学图书馆 西安 710051
Literature Recommendation Using Evolution Patterns
Wei Meng1,2
1. School of Information Management, Wuhan University, Wuhan 430072, China;
2. Air Force Engineering University Library, Xi'an 710051, China
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摘要 

[目的] 引导读者就某一话题由浅入深、循序渐进地进行文献的检索与阅读。[应用背景] 文献推荐服务一直是数字图书馆的核心业务之一,对读者进行文献的查询和检索起着重要的作用。[方法] 提出一种基于用户搜索行为演化模式的文献推荐方法(CALL)。从文献库与检索日志中提取文献、读者与检索日志特征;将文献分为n个阅读阶段,利用最长公共子序列算法从三个特征中寻找到文献阅读序列,并将超过一定长度与频率的文献序列作为推荐结果。[结果] 在真实文献库与检索日志数据集上进行广泛实验,验证所提出方法的准确性、执行效率与可扩展性等方面的性能,达到丰富数字图书馆文献推荐的目的。[结论] 本研究可以增强现有数字图书馆的文献推荐工作的性能与效率,促使文献推荐工作向多样化方向发展。

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关键词 演化模式数字图书馆文献推荐    
Abstract

[Objective] To help users retrieve and read the literature of one topic from the shallower to the deeper. [Context] Literature recommendation service is one of the core businesses in digital library, and it plays an important role in literature searching and querying for the readers. [Methods] This paper introduces a user searching behaviour Common evolution pAtterns based Literature retrievaL method (CALL for short). First, it extracts the features of literature, readers and retrieval logs, then it clusters the literature into n stages, further uses longest common subsequence method to mine the frequent article name sequences that are greater than the thresholds of length and frequency, finally it outputs the frequent subsequences from the above stage as the recommendation results. [Results] The author conducts extensive experiments on real literature and retrieval log datasets, and results demonstrate the accuracy, efficiency and scalability of the methods. And it can enrich the performance of recommendation of digital library. [Conclusions] The proposed methods can greatly enhance the efficiency of the existing literature recommendation systems, and make the direction of literature recommendation be diversified.

Key wordsEvolution pattern    Digital library    Literature recommendation
收稿日期: 2013-10-20     
:  TP393  
基金资助:

本文系国家青年自然科学基金项目“基于在线判别学习的鲁棒视觉跟踪算法研究”(项目编号:61203268)的研究成果之一。

通讯作者: 尉萌 E-mail:weimeng.k@126.com     E-mail: weimeng.k@126.com
引用本文:   
尉萌. 利用演化模式做文献推荐[J]. 现代图书情报技术, 2014, 30(4): 20-26.
Wei Meng. Literature Recommendation Using Evolution Patterns. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2014.04.04.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.04.04

[1] 杨艳,李建中.数字图书馆中基于文献拓扑的个性化推荐技术[J].计算机研究与发展,2004,41(S):472-477.(Yang Yan,Li Jianzhong.Topology-based Paper Recommendation in Digital Library[J].Journal of Computer Research and Development,2004,41(S):472-477.)
[2] 陈祖琴,张惠玲,葛继科,等.基于加权关联规则挖掘的相关文献推荐[J].现代图书情报技术,2007(10):57-61.(Chen Zuqin,Zhang Huiling,Ge Jike,et al.Related Document Recommending Based on Weighted Association Rule Mining[J].New Technology of Library and Information Service,2007 (10):57-61.)
[3] 王磊.协同推荐技术及其在科技文献个性化推荐系统中的应用研究[D].南京:南京理工大学,2007.(Wang Lei.Collabo­rative Recommendation and Its Application in Personalized Recommendation System of Scientific Literatures[J].Nanjing:Nanjing University of Science and Technology,2007.)
[4] 陈青松.期刊网针对特定课题的文献推荐策略[J].图书情报知识,2009(6):70-74.(Chen Qingsong.Reference Reco­mmendation Strategy of Digital Resources Related to a Topic[J].Document,Information &Knowledge,2009(6):70-74.)
[5] 黄珍.基于数据挖掘的文献自动推荐研究[D].武汉:华中师范大学,2009.(Huang Zhen.Research of Reference Auto­matic Recommendation Based on Data Mining[D].Wuhan:Central China Normal University,2009.)
[6] 张琪,章颖华.情境感知的科技文献协同推荐方法研究[J].现代图书情报技术,2012(2):10-17.(Zhang Qi,Zhang Yinghua.Research on an Approach of Context Aware Collaborative Recommend for Scientific &Technical Literatures[J].New Technology of Library and Information Service,2012 (2):10-17.)
[7] 赖院根.科技文献跨语言推荐模型研究[J].中国图书馆学报,2012,38(2):70-77.(Lai Yuangen.Study on Cross-language Personalized Recommendation of Academic Literatures[J].Journal of Library Science in China,2012,38(2):70-77.)
[8] Li L,Wang D,Li T,et al.SCENE:A Scalable Two-Stage Personalized News Recommendation System[C].In:Procee­dings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).2011:125-134.
[9] Abbar S,Amer-Yahia S,Indyk P.Real-Time Recommendation of Diverse Related Articles[C].In:Proceedings of the 22nd International World Wide Web Conference (WWW).2013:1-12.
[10] Zhou K,Yang S H,Zha H.Functional Matrix Factorizations for Cold-Start Recommendation[C].In:Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).2011:315-324.
[11] Liu X,Aberer K.SoCo:A Social Network Aided Context-Aware Recommender System[C].In:Proceedings of the 22nd Interna­tional World Wide Web Conference (WWW).2013:781-802.
[12] Chen W,Hsu W,Lee M L.Making Recommendations from Multiple Domains[C].In:Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).2013:892-900.
[13] Niemann K,Wolpers M.A New Collaborative Filtering Approach for Increasing the Aggregate Diversity of Recomm­ender Systems[C].In:Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).2013:955-963.
[14] Agrawal R,Srikant R.Fast Algorithms for Mining Association Rules in Large Databases[C].In:Proceedings of the 20th International Conference on Very Large Data Bases (VLDB).1994:487-499.
[15] Han J,Pei J,Yin Y.Mining Frequent Patterns without Candidate Generation[C].In:Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD).2000:1-12.
[16] Hirschberg D S.Algorithms for the Longest Common Subsequence Problem[J].Journal of the ACM (JACM),1977,24(4):664-675.
[17] 最长公共子序列[EB/OL].[2013-12-18].http://blog.csdn.net/yysdsyl/article/details/4226630.(LCS[EB/OL].[2013-12-18].http://blog.csdn.net/yysdsyl/article/details/4226630.)
[18] ICTCLAS4j分词系统[EB/OL].[2013-12-18].https://code.google.com/p/ictclas4j/.(ictclas4j[EB/OL].[2013-12-18].https://code.google.com/p/ictclas4j/.)

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