Recommender Systems over Wireless: Challenges and Opportunities

被引：0
|
作者：
Song, Linqi [1 ]
Fragouli, Christina [2 ]
Shah, Devavrat [3 ]
机构：
[1] City Univ Hong Kong, Hong Kong, Peoples R China
[2] Univ Calif Los Angeles, Los Angeles, CA 90095 USA
[3] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA
来源：
2018 IEEE INFORMATION THEORY WORKSHOP (ITW) | 2018年
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
We consider wireless recommender systems that need to learn the user preferences (explore) and use them to accordingly decide what are the most profitable recommendations to make (exploit), under bandwidth constraints. We propose a graph-based scheme that leverages user side information and coding to efficiently exploit and explore over wireless, and evaluate its performance.
引用
收藏
页码：420 / 424
页数：5
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