Beyond Data: From User Information to Business Value through Personalized Recommendations and Consumer Science

被引：19
作者：
Amatriain, Xavier [1 ]
机构：
[1] Netflix, Los Gatos, CA 95032 USA
来源：
PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13) | 2013年
关键词：
Recommender Systems; Personalization; Machine Learning; Big Data;
D O I：
10.1145/2505515.2514701
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Since the Netflix $1 million Prize, announced in 2006, Netflix has been known for having personalization at the core of our product. Our current product offering is nowadays focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search. In this paper I will discuss the different approaches we follow to deal with these large streams of user data in order to extract information for personalizing our service. I will describe some of the machine learning models used, and their application in the service. I will also describe our datadriven approach to innovation that combines rapid offline explorations as well as online A/B testing. This approach enables us to convert user information into real and measurable business value.
引用
收藏
页码：2201 / 2207
页数：7
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