Evaluation and Functionality Stems Extraction for App Categorization on Apple iTunes Store by Using Mixed Methods :

• 발행기관: 한국IT서비스학회
• 저자명: Chao Zhang Lili Wan
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제17권 제2호, 111~128쪽, 전체 18쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2018.06.30

영문 초록

About 3.9 million apps and 24 primary categories can be approved on Apple iTunes Store. Making accurate categorization can potentially receive many benefits for developers, app stores, and users, such as improving discoverability and receiving long-term revenue. However, current categorization problems may cause usage nefficiency and confusion, especially for cross-attribution, etc. This study focused on evaluating the reliability of app categorization on Apple iTunes Store by using several rounds of inter-rater reliability statistics, locating categorization problems based on Machine Learning, and making more accurate suggestions about representative functionality stems for each primary category. A mixed methods research was performed and total 4905 popular apps were observed. The original categorization was proved to be substantial reliable but need further improvement. The representative functionality stems for each category were identified. This paper may provide some fusion research experience and methodological suggestions in categorization research field and improve app store’s categorization in discoverability.

목차

1. Introduction

2. Literature Review

3. Research Approach

4. Data Analysis and Result

5. Discussion

6. Conclusion and Future Research

References

About the Authors

키워드

Mobile Application App Categorization Inter-Rater Reliability Functionality Stem Tf-Idf Machine Learning Lemmatization Natural Language Processing