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Mahout构建图书推荐系统

Jun 07, 2016 pm 04:34 PM
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Hadoop家族系列文章,主要介绍Hadoop家族产品,常用的项目包括Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa,新增加的项目包括,YARN, Hcatalog, Oozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, Hue等。 从2011年开

Hadoop家族系列文章,主要介绍Hadoop家族产品,常用的项目包括Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa,新增加的项目包括,YARN, Hcatalog, Oozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, Hue等。 从2011年开始,中国进入大数据风起云涌的时代,以Hadoop为代表的家族软件,占据了大数据处理的广阔地盘。开源界及厂商,所有数据软件,无一不向Hadoop靠拢。Hadoop也从小众的高富帅领域,变成了大数据开发的标准。在Hadoop原有技术基础之上,出现了Hadoop家族产品,通过“大数据”概念不断创新,推出科技进步。 作为IT界的开发人员,我们也要跟上节奏,抓住机遇,跟着Hadoop一起雄起! 关于作者: 张丹(Conan), 程序员Java,R,PHP,Javascript weibo:@Conan_Z blog:?http://blog.fens.me email: bsspirit@gmail.com 转载请注明出处: http://blog.fens.me/hadoop-mahout-recommend-book/ 前言 本文是Mahout实现推荐系统的又一案例,用Mahout构建图书推荐系统。与之前的两篇文章,思路上面类似,侧重点在于图书的属性如何利用。本文的数据在自于Amazon网站,由爬虫抓取获得。 目录 项目背景 需求分析 数据说明 算法模型 程序开发 1. 项目背景 Amazon是最早的电子商务网站之一,以网上图书起家,最后发展成为音像,电子消费品,游戏,生活用品等的综合性电子商务平台。Amazon的推荐系统,是互联网上最早的商品推荐系统,它为Amazon带来了至少30%的流量,和可观的销售利润。 如今推荐系统已经成为电子商务网站的标配,如果还没有推荐系统都不好意思,说自己是做电商的。 2. 需求分析 推荐系统如此重要,我们应该如果理解? 打开Amazon的Mahout In Action图书页面: http://www.amazon.com/Mahout-Action-Sean-Owen/dp/1935182684/ref=pd_sim_b_1?ie=UTF8&refRID=0H4H2NSSR8F34R76E2TP 网页上的元素: 广告位:广告商投放广告的位置,网站可以靠网络广告赚钱,一般是网页最好的位置。 平均分:用户对图书的打分 关联规则:通过关联规则,推荐位 协同过滤:通过基于物品的协同过滤算法的,推荐位 图书属性:包括页数,出版社,ISBN,语言等 …
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