Recommending the World's Knowledge: Application of Recommender Systems at Quora

被引:10
|
作者
Yang, Lei [1 ]
Amatriain, Xavier [1 ]
机构
[1] Quora Inc, 650 Castro St, Mountain View, CA 94041 USA
关键词
Recommender systems; ranking; collaborative filtering; machine learning; personalization;
D O I
10.1145/2959100.2959128
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
At Quora, our mission is to share and grow the world's knowledge. Recommender systems are at the core of this mission: we need to recommend the most important questions to people most likely to write great answers, and recommend the best answers to people interested in reading them. Driven by the above mission statement, we have a variety of interesting and challenging recommendation problems and a large, rich data set that we can work with to build novel solutions for them. In this talk, we will describe several of these recommendation problems and present our approaches solving them.
引用
收藏
页码:389 / 389
页数:1
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