Mining target users for online marketing based on App Store data

It is well known that the key issue of online marketing is to accurately find the target user groups for the corresponding advertisements. Traditionally, the advertising products target user groups based on search keywords (e.g. AdWords), page visiting (e.g. AdSense), and etc. In this work, we explore a new targeting strategy - targeting users based on their downloaded apps. Specifically, we make use of a subset of the data from the Huawei App Store, consisting of 20,169,033 users and 122,875 apps with 453,346,250 downloads during one year. For each marketing job, the advertiser only need to label a small set of apps, usually around 10 apps, that the target users might be interested in. Our system xRank will automatically find a list of top potential target users for the advertiser. We implement xRank with very efficient performance on the top of Hadoop to be capable for a real web-scale dataset, and then conducted our system to several real marketing tasks. The results show that, for each marketing task, with only a few labels, xRank can effectively find a precise target group of users, and can also significantly improved the effectiveness of our online marketing compared to the rule-based approaches in the current system.