Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network
| Autor: | Jens Helge Reelfs, Oliver Hohlfeld, Niklas Henckell, Max Bergmann |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Computer Science - Machine Learning Social network Binary decision diagram business.industry Computer science media_common.quotation_subject Computer Science - Social and Information Networks Disjoint sets Machine learning computer.software_genre Regression Machine Learning (cs.LG) Random forest Feature (machine learning) Off the shelf Quality (business) Artificial intelligence business computer media_common |
| Zdroj: | WWW (Companion Volume) |
| DOI: | 10.1145/3442442.3451887 |
| Popis: | In this work, we predict the user lifetime within the anonymous and location-based social network Jodel in the Kingdom of Saudi Arabia. Jodel's location-based nature yields to the establishment of disjoint communities country-wide and enables for the first time the study of user lifetime in the case of a large set of disjoint communities. A user's lifetime is an important measurement for evaluating and steering customer bases as it can be leveraged to predict churn and possibly apply suitable methods to circumvent potential user losses. We train and test off the shelf machine learning techniques with 5-fold crossvalidation to predict user lifetime as a regression and classification problem; identifying the Random Forest to provide very strong results. Discussing model complexity and quality trade-offs, we also dive deep into a time-dependent feature subset analysis, which does not work very well; Easing up the classification problem into a binary decision (lifetime longer than timespan $x$) enables a practical lifetime predictor with very good performance. We identify implicit similarities across community models according to strong correlations in feature importance. A single countrywide model generalizes the problem and works equally well for any tested community; the overall model internally works similar to others also indicated by its feature importances. Comment: To appear at the WWW'21 LocWeb Workshop |
| Databáze: | OpenAIRE |
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