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About User Profile
This document discusses using machine learning techniques to personalize mobile advertising based on a user's installed apps. The authors collected data on millions of users' app installations and interactions with different types of mobile ads. They used k-means clustering to group users into 10 profiles based on their installed apps. They found clear differences in how the profiles interacted with finance, entertainment and lifestyle ads. Association rule mining also revealed relationships between user profile, time of day, number of apps installed, and type of ad interaction. The results suggest targeting ads based on users' app data could improve interaction rates.
Detailed Information
| Author: | ['Gustav Steiner'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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