Investigating user profiles for mobile application categories and providing recommendations to software engineering

Aker, Gokhan (2018). Investigating user profiles for mobile application categories and providing recommendations to software engineering. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Investigating user profiles for mobile application categories and providing recommendations to software engineering
Author/s:
  • Aker, Gokhan
Contributor/s:
  • Segovia Pérez, Francisco Javier
Item Type: Thesis (Master thesis)
Masters title: Ingeniería del Software
Date: July 2018
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Mobile apps are a type of software applications developed for use on mobile devices such as smartphones and tablets. Once developed, an app is made available for users via an application distribution platform, commonly known as an app store. (Lim, Bentley, Kanakam, Ishikawa & Honiden, 2015). Mobile apps in an app store are presented via mobile application categories. List of app categories may change from app store to app store. A mobile app can be in more than one category. For instance: A photo editing and sharing mobile app can be in both social networking and photo & video app category. Generally a mobile application’s success is measured by its number of downloads. App stores and mobile app developers want more users to download their apps. In order to do this, they prepare a marketing strategy and promote their apps through different platforms. (E.g: Social Media, Search Ads, TV Advertising, etc). To make this app promotion strategy more effective they need to identify best user profiles for their app category. In other words, they need to identify user profiles which have a high percentage of downloading apps from that app category. My thesis work is about using data mining to identify these user profiles for each mobile app category. In order to apply data mining techniques I needed to find an open source dataset about user profiles and their preferences about mobile apps. Luckily, I found a research about this topic including a survey and an open source dataset. Dataset is filled with users’ answers to survey questions. This academic work is performed from researchers of University of College London and National Institute of Informatics, Japan. These researchers published an academic paper which is about mobile app user behaviour based on country differences. (Lim, Bentley, Kanakam, Ishikawa & Honiden, 2015). Throughout data mining and master thesis process I followed CRISP-DM Methodology. CRISP-DM methodology is a structured way of planning a data mining project It consists six different stages: Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Deployment. (Vorhies, 2016) I followed methodology in its exact order. However, I wrote only one deployment section which covers all app categories.

More information

Item ID: 51627
DC Identifier: http://oa.upm.es/51627/
OAI Identifier: oai:oa.upm.es:51627
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 13 Jul 2018 10:42
Last Modified: 13 Jul 2018 10:42
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