基于读者信息挖掘的图书馆资源推荐自动模型研究

Automatic Recommendation Model of Library Resource Based on Readers' Information Mining

[目的/意义]提出基于读者信息挖掘的图书馆资源自动推荐模型,解决读者需求挖掘中的信息来源单一、挖掘深度不足等问题,推动图书馆信息处理领域的研究深入开展。[方法/过程]基于网络爬虫技术和读者需求的分散现状,设计了新型的馆藏资源自动推荐模型,给出了该模型的组成结构、功能模块以及处理流程,并用网络爬虫技术解决了读者信息的自动采集与挖掘问题。[结果/结论]实验证明,该模型具有较高的读者需求覆盖度、精确度,有一定的实用价值。

[Purpose/significance]The paper is to put forwards an automatic recommendation model of library resource based on readers’information mining to solve the problems of single information source and insufficient mining depth,and promote the development of information processing research in library.[Method/process]The paper designs a novel automatic recommendation model of library resources based on the status quo of web crawler technology and decentralized readers’demands,shows the composition and structure,functional modules and processing flow,and uses web crawler technology to settle the problems of automatic collection and mining.[Result/conclusion]Experiment results show that the model has high readers’demand coverage,accuracy and practical value.