Modeling and simulation of mobile apps user behavior
| dc.contributor.author | Dharmasena, Ranasinghe Arachchige Isuru Harsha | |
| dc.contributor.examiningcommittee | Domaratzki, Mike (Computer Science) | en_US |
| dc.contributor.examiningcommittee | Turgeon, Max (Statistics and Computer Science) | en_US |
| dc.contributor.supervisor | Muthukumarana, Saman (Statistics) | en_US |
| dc.date.accessioned | 2020-10-30T19:22:04Z | |
| dc.date.available | 2020-10-30T19:22:04Z | |
| dc.date.copyright | 2020-09-10 | |
| dc.date.issued | 2020 | en_US |
| dc.date.submitted | 2020-09-10T17:58:53Z | en_US |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Mobile applications have become a vital part in modern businesses where products and services are offered in real-time. As many people have adopted to mobile apps, it is not uncommon that some of the applications are used for a few times and then abandoned. This ''churning" effect on mobile apps has become a wide topic of interest among businesses to understand the factors affecting the user abandonment. This includes predicting and identifying the abandoning users beforehand to actively engage users to have more active and loyal app users. There is often a class imbalance problem where the retained user group is the minority class. We study and assess several over-sampling methods and under-sampling methods combined with several classification methods to improve the prediction ability and model performance of mobile app user retention using data available from a local mobile app developing company. We then discuss a non-parametric hypothesis testing strategy to compare similar ROC curves obtained by different re-sampling strategies. Finally, we propose a Bayesian network to assess which features in a particular mobile App are affecting the retention of an App user. Re-sampling techniques are then used to improve the performance of the Bayesian network and we use Structural Hamming Distances (SHD) to distinguish similar Bayesian network structures. | en_US |
| dc.description.note | February 2021 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/35124 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Churn prediction | en_US |
| dc.subject | Data imbalance | en_US |
| dc.subject | Over-sampling | en_US |
| dc.subject | Under-sampling | en_US |
| dc.subject | Bayesian network | en_US |
| dc.title | Modeling and simulation of mobile apps user behavior | en_US |
| dc.type | master thesis | en_US |