User Embeddings Based on Mobile App Behavior Data

被引：0

作者：

Singla, Kushal ^{[1
]}

Abrol, Satyen ^{[1
]}

Park, Sungdeuk ^{[2
]}

机构：

[1] Samsung R&D Inst, Bangalore, Karnataka, India

[2] Samsung Elect, Suwon, South Korea

来源：

HOW AI IMPACTS URBAN LIVING AND PUBLIC HEALTH, ICOST 2019 | 2019年 / 11862卷

关键词：

User2vec; AutoEncoder; User embedding;

D O I：

10.1007/978-3-030-32785-9_16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider a smart phone scenario with a number of apps used by a user. The app usage data provides information about the user behavior, which can be used to identify the user demographics and interest and in turn is used to find similar users. In this paper, we propose a method to generate a latent space user embedding using the user app usage data, which is a dense low-dimensional representation of the user. This representation is used for low latency user similarity computation and acts as the user feature representation in user demographics prediction models.

引用

页码：183 / 189

页数：7

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