传统图书馆服务缺乏个性化设置,无法充分利用资源进行准确书目推荐,为了改善这一问题,提出基于读者个性化特征数据挖掘的图书馆书目推荐系统.根据读者类聚特点与数据关联规则,设计节目个性化推荐系统,并将挖掘出的关联规则应用到推荐服务当中;根据挖掘流程可得到大量数据,并对多余数据进行清理,不完整数据进行补充,计算支持度和置信度;采用基于读者个性化特征数据挖掘图书馆书目并进行推荐,由此完成图书馆书目推荐.通过实验分析可知,该推荐方法可充分利用图书馆资源,快速、准确完成书目推荐.%The traditional library service lacks personalized setting,and cannot make full use of resources to conduct cata-logue recommendation accurately. To resolve this problem,library catalogue recommendation based on readers′ personalized fea-ture data mining is proposed. According to reader clustering characteristics and data association rules,a personalized program recommendation system is designed,and the mined association rules are applied to recommendation service. A large amount of data can be obtained according to the mining process,with redundant data cleaned and incomplete data supplemented,so as to calculate the support degree and confidence coefficient. Readers′ personalized feature data is used to mine and recommend li-brary catalogue,so as to complete library catalogue recommendation. The experimental analysis shows that this recommendation method can make full use of library resources and complete catalogue recommendation quickly and accurately.
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