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Mahout构建图书推荐系统

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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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