用高斯混合模型估算家庭电视用户数量，提升群组推荐精准度。
Estimating the number of household TV profiles based in customer behaviour using Gaussian mixture model averaging
- 基于高斯混合模型平均法，推断家庭中观看者的个体数量。
- 在约50万条真实观看数据上验证，可捕捉用户特征随时间变化。
- 适合做个性化推荐系统的研发人员参考。
当前电视用户面临众多直播频道与点播服务的选择，提供个性化内容发现体验对电视服务商至关重要。然而，准确理解用户行为与偏好是关键挑战，尤其是在多用户共用设备的家庭场景中。如何识别并整合多个个体的观看习惯以实现群体推荐，仍缺乏有效方法。本文提出一种新框架：使用高斯混合模型平均法估计家庭电视用户数量，并结合贝叶斯随机游走模型量化不确定性。该方法基于约50万条真实客户观看数据进行测试，结果表明，结合特定特征后，可有效估计家庭中电视用户数量及其动态变化，同时提供不确定性评估，为群体个性化推荐提供支持。
原文摘要 · Abstract (English)
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.
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