协作性过滤 简单理解从众多用户中先搜索出与目标用户'品味'相似的部分人,然后考察这部分人的偏爱,根据偏爱结果为用户做推荐.这个过程也成为基于用户的协作性过滤(user_based collaborative filtering). 以推荐电影为例 数据集 偏好数据集,嵌套字典,格式为:{用户:{电影名称:评分}}. critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5, 'Just My Luck':…

[原文链接:http://engineering.richrelevance.com/recommendations-thompson-sampling/.] [本文链接:http://www.cnblogs.com/breezedeus/p/3775339.html,转载请注明出处] Recommendations with Thompson Sampling 06/05/2014 • Topics: Bayesian, Big data, Data Science by Sergey Fel…

<Programming Collective Intelligence> Chapter2:Making Recommendations 欧几里得距离评价 皮尔逊相关度评价 它相比于欧几里德距离评价,其在数据不是很规范的时候(比如,影评者对影片的评价总是相对于平均水平偏离很大的时候),会给出更好的结果.       如果某人总是倾向于给出比另一个人更高的分值,而两者的分值之差又始终保持一致,则他们依然可能会存在很好的相关性.而欧几里德距离评价会因为一个人的评价之中比另外一个人的更为“严格”(…

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by Xavier Amatriain and Justin Basilico (Personalization Science and Engineering) In part one of this blog post, we detailed the different components of Netflix personalization. We also explained how Netflix personalization, and the service as a whol…

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[论文标题]Amazon.com recommendations: item-to-item collaborative filtering (2003,Published by the IEEE Computer Society) [论文作者]Greg Linden,Brent Smith,and Jeremy York • Amazon.com [论文链接]Paper (5-pages // Double column) [Info] 亚马逊是推荐系统领域最具代表性的公司之一.(还有一家是N…