利用演化模式做文献推荐  

Literature Recommendation Using Evolution Patterns

作　　者：尉萌 

机构地区：[1]武汉大学信息管理学院 [2]空军工程大学图书馆

出　　处：《现代图书情报技术》2014年第4期20-26,共7页New Technology of Library and Information Service

基　　金：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

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。[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.

关 键 词：演化模式 数字图书馆 文献推荐 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]