# A relevance-scalability-interpretability tradeoff with temporally evolving user personas

## TL;DR

This paper introduces a method to create user personas based on tenure and behavior in VoD streaming, balancing relevance, scalability, and interpretability, and demonstrates their use in improving CTR prediction.

## Contribution

The work presents a novel approach to derive evolving user personas without explicit profiles, capturing behavioral maturation and migration, and explores the trade-off among relevance, scalability, and interpretability.

## Key findings

- Personas show stability over time at the population level.
- Individual user labels exhibit migration and evolution.
- Using personas improves CTR predictive models.

## Abstract

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.