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.

翻译：当今电视用户面临来自众多直播频道和点播服务的多种选择。为电视提供商而言，提供能够节省用户内容发现时间的个性化体验至关重要。然而，对其行为和偏好的可靠理解是关键所在。在为电视创建个性化推荐时，最大的挑战在于理解多人观看时家庭内部的观看行为。其目标是检测并整合个人画像，从而为群体观看提供更优的个性化推荐。我们面临的挑战是，我们几乎无法明确获知任何时刻设备前观看者的具体身份（个人或群体）。此外，我们缺乏整合多个个人画像以优化群体观看推荐的方法。本文提出一种新颖框架：使用高斯混合模型平均来获取家庭电视画像数量的点估计，并采用贝叶斯随机游走模型引入不确定性。我们应用真实用户数据验证了该方法，其电视观看数据总计约五十万条观测记录。结果表明，将本框架与所选特征相结合，能够有效估计家庭电视画像的数量及其特征，包括随时间的变化趋势以及不确定性的量化。