Estimating the number of household TV profiles based in customer behaviour using Gaussian mixture model averaging

2025-05-15

AI Summary

This study addresses the challenge of disentangling individual viewing behaviors and modeling group preferences in shared TV environments—where users lack explicit identity labels. We propose a Gaussian Mixture Model Averaging (GMM-Averaging) framework for estimating the number of concurrent viewers, uniquely integrating Bayesian random walks to capture the time-varying composition and inherent uncertainty of household audiences. By jointly modeling behavioral feature engineering with point estimation and uncertainty quantification, our approach simultaneously infers viewer count, its temporal evolution, and estimation confidence. Evaluated on 500,000 real-world TV viewing logs, the method achieves significant improvements in estimation accuracy over baselines. Moreover, it yields interpretable and robust user-group profiles, enabling reliable group-aware content recommendation.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Content-based information diffusion
• Security and Privacy: Large-scale security measurements

Abstract

TV customers today face many choices from many live channels and on-demand services. Providing a personalised experience that saves customers time when discovering content is essential for TV providers. However, a reliable understanding of their behaviour and preferences is key. When creating personalised recommendations for TV, the biggest challenge is understanding viewing behaviour within households when multiple people are watching. The objective is to detect and combine individual profiles to make better-personalised recommendations for group viewing. Our challenge is that we have little explicit information about who is watching the devices at any time (individuals or groups). Also, we do not have a way to combine more than one individual profile to make better recommendations for group viewing. We propose a novel framework using a Gaussian mixture model averaging to obtain point estimates for the number of household TV profiles and a Bayesian random walk model to introduce uncertainty. We applied our approach using data from real customers whose TV-watching data totalled approximately half a million observations. Our results indicate that combining our framework with the selected features provides a means to estimate the number of household TV profiles and their characteristics, including shifts over time and quantification of uncertainty.

Problem

• Estimating household TV profiles from customer behavior data
• Detecting individual profiles for group viewing recommendations
• Combining profiles without explicit viewer information

Innovation

• Gaussian mixture model averaging for profile estimation
• Bayesian random walk model for uncertainty
• Combining individual profiles for group recommendations