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Conference Paper: ITunes' app ranking algorithm unveiled: A ranking model for mobile apps
| Title | ITunes' app ranking algorithm unveiled: A ranking model for mobile apps |
|---|---|
| Authors | |
| Issue Date | 2013 |
| Citation | WITS 2013 - 23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, 2013 How to Cite? |
| Abstract | Smartphone based mobile apps are the fastest growing consumer product of the decade. Despite the widespread popularity and growth of mobile apps, the native store's app ranking algorithm is a well-guarded secret. Thus, app constituents face numerous hurdles in positioning their apps and identifying their target audience in the hypercompetitive apps market. This paper takes an important step in conceptualizing the app ranking model as a function of user WOM, developer popularity and socio demographics of users. We demonstrate our model using lifecycle data of iTunes apps. Implications for research and practice are discussed. © Thirty Fourth International Conference on Information Systems, Milan 2013. |
| Persistent Identifier | http://hdl.handle.net/10722/277003 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Yoganathan, Duwaraka | - |
| dc.contributor.author | Sangaralingam, Kajanan | - |
| dc.contributor.author | Phan, Tuan | - |
| dc.date.accessioned | 2019-09-18T08:35:18Z | - |
| dc.date.available | 2019-09-18T08:35:18Z | - |
| dc.date.issued | 2013 | - |
| dc.identifier.citation | WITS 2013 - 23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, 2013 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/277003 | - |
| dc.description.abstract | Smartphone based mobile apps are the fastest growing consumer product of the decade. Despite the widespread popularity and growth of mobile apps, the native store's app ranking algorithm is a well-guarded secret. Thus, app constituents face numerous hurdles in positioning their apps and identifying their target audience in the hypercompetitive apps market. This paper takes an important step in conceptualizing the app ranking model as a function of user WOM, developer popularity and socio demographics of users. We demonstrate our model using lifecycle data of iTunes apps. Implications for research and practice are discussed. © Thirty Fourth International Conference on Information Systems, Milan 2013. | - |
| dc.language | eng | - |
| dc.relation.ispartof | WITS 2013 - 23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits | - |
| dc.title | ITunes' app ranking algorithm unveiled: A ranking model for mobile apps | - |
| dc.type | Conference_Paper | - |
| dc.identifier.scopus | eid_2-s2.0-84907398574 | - |
| dc.identifier.spage | null | - |
| dc.identifier.epage | null | - |