Mining target users for online marketing based on App Store data

By Xiuqiang He, Wenyuan Dai, Guoxiang Cao, Ruiming Tang, Mingxuan Yuan, Qiang Yang. 2015

10 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Yue Jia
2. Yuanyuan Zhang
3. Mark Harman
4. Neil Zhenqiang Gong
5. Federica Sarro
6. William J. Martin
7. Minghong Fang
8. Zhenguo Li

In Marketing

1. Neil Zhenqiang Gong
2. Xiuqiang He
3. Guolei Yang

In Sociology and Political Science

1. Neil Zhenqiang Gong
2. Yue Jia
3. Mark Harman
4. Yuanyuan Zhang

Citations in the corpus

• Poisoning Attacks to Graph-Based Recommender Systems (2018, methodology): “…recommender systems are deployed by Netflix to recommend movies, association-rule-based recommender systems are deployed by YouTube to recommend videos [6], and graph-based recommender systems are deployed by eBay [25, 26] and Huawei App Store (a big app store in China) [12, 13].”
• A Survey of App Store Analysis for Software Engineering (2017, background): “[93] trained a system for targeting users for advertising, with a dataset containing app install data on a per-user basis, con-”
• A Graph-Based Push Service Platform (2016, methodology): “We use CTR (click-through ratio) [7] and also DTR (download-through ratio) as the online evaluation metrics, where CTR = |Users who clicked advertised app|/|Users who received ads| and DTR = |Users who downloaded advertised apps|/|Users who received ads|.”