Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review
The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern un...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8720158/ |
| _version_ | 1831679686642499584 |
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| author | Bin Guo Yi Ouyang Tong Guo Longbing Cao Zhiwen Yu |
| author_facet | Bin Guo Yi Ouyang Tong Guo Longbing Cao Zhiwen Yu |
| author_sort | Bin Guo |
| collection | DOAJ |
| description | The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented. |
| first_indexed | 2024-12-20T05:23:59Z |
| format | Article |
| id | doaj.art-78123300ae5547c9b59e1941480b9f56 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:23:59Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-78123300ae5547c9b59e1941480b9f562022-12-21T19:51:56ZengIEEEIEEE Access2169-35362019-01-017685576857110.1109/ACCESS.2019.29183258720158Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A ReviewBin Guo0https://orcid.org/0000-0001-7631-3386Yi Ouyang1Tong Guo2Longbing Cao3Zhiwen Yu4School of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaAdvanced Analytics Institute, University of Technology Sydney, Ultimo, NSW, AustraliaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaThe mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.https://ieeexplore.ieee.org/document/8720158/App marketinguser profilingpopularity predictionapp recommendationusage pattern miningmobile crowdsourcing |
| spellingShingle | Bin Guo Yi Ouyang Tong Guo Longbing Cao Zhiwen Yu Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review IEEE Access App marketing user profiling popularity prediction app recommendation usage pattern mining mobile crowdsourcing |
| title | Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review |
| title_full | Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review |
| title_fullStr | Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review |
| title_full_unstemmed | Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review |
| title_short | Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review |
| title_sort | enhancing mobile app user understanding and marketing with heterogeneous crowdsourced data a review |
| topic | App marketing user profiling popularity prediction app recommendation usage pattern mining mobile crowdsourcing |
| url | https://ieeexplore.ieee.org/document/8720158/ |
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