Association rule mining for building book recommendation system in online public access catalog

S. Mariana, I. Surjandari, Arian Dhini, Asma Rosyidah, P. Prameswari
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引用次数: 6

Abstract

Improvement in service quality in Online Public Access Catalog (OPAC) of Universitas Indonesia's library is required due to its increasing use. Book recommendation system is one of the efforts that is conducted by Universitas Indonesia to improve it by providing related books the user may need. Thus, it is required to know how the books correlation is. This research uses data mining to process numerous data by using association analysis in loan records. It is able to find interesting relationship in a large set of data. In this research, there are two approaches in association analysis used to mine the association rules namely frequent itemset mining and infrequent itemset mining. Each approach is applied through an algorithm and both have showed its own results. Then, these results are evaluated and compared to find best rules to be input of book recommendation system.
基于关联规则挖掘的在线公共目录图书推荐系统构建
印尼大学图书馆在线公共访问目录(OPAC)的使用日益增加,需要提高其服务质量。图书推荐系统是印尼大学通过提供用户可能需要的相关书籍来改进它的努力之一。因此,需要知道书籍的相关性是如何的。本研究通过对贷款记录进行关联分析,利用数据挖掘技术对大量数据进行处理。它能够从大量的数据中发现有趣的关系。在本研究中,关联分析中有两种方法用于挖掘关联规则,即频繁项集挖掘和非频繁项集挖掘。每种方法都是通过一种算法应用的,并且都显示了自己的结果。然后,对这些结果进行评估和比较,以找到最佳规则作为图书推荐系统的输入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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