2017/04/25 by Snigdha Panigrahi, Nadia Fawaz, Panigrahi, Snigdha +1
Computer Science · Medicine · Social Sciences · #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (stat.ML) #Persona Design and Applications
paper · pdf · doi:10.48550/arxiv.1704.07554
openalex publication_date 2017/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The current work characterizes the users of a VoD streaming space through user-personas based on a tenure timeline and temporal behavioral features in the absence of explicit user profiles. A combination of tenure timeline and temporal characteristics caters to business needs of understanding the evolution and phases of user behavior as their accounts age. The personas constructed in this work successfully represent both dominant and niche characterizations while providing insightful maturation of user behavior in the system. The two major highlights of our personas are demonstration of stability along tenure timelines on a population level, while exhibiting interesting migrations between labels on an individual granularity and clear interpretability of user labels. Finally, we show a trade-off between an indispensable trio of guarantees, relevance-scalability-interpretability by using summary information from personas in a CTR (Click through rate) predictive model. The proposed method of uncovering latent personas, consequent insights from these and application of information from personas to predictive models are broadly applicable to other streaming based products.