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

TL;DR

This paper introduces a novel Gaussian mixture model averaging framework to estimate the number of household TV profiles from customer viewing data, enhancing personalized recommendations despite limited explicit information.

Contribution

The paper presents a new probabilistic framework combining Gaussian mixture models and Bayesian methods to estimate household profiles from viewing data, addressing a key challenge in personalization.

Findings

Successfully estimated the number of profiles in real customer data

Quantified uncertainty in profile estimation over time

Improved personalization potential through better profile detection

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.