观众预测和分析的人工智能,为创新的电视内容推荐服务提供动力

L. Nixon, K. Ciesielski, Basil Philipp
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引用次数: 6

摘要

在当代电视观众预测中,异常值被认为仅仅是可以用于预测的周期性趋势和季节性成分中的异常值。在ReTV项目中,我们希望提供更准确的观众预测,以便为电视内容推荐提供创新服务。本文提出了一种识别异常值来源的概念,并将电视内容类别和事件发生作为训练电视观众预测的附加特征。我们将展示这如何提高观众预测的准确性。最后,我们概述了这项工作如何与支持人工智能的受众分析相结合,从而为新的内容推荐服务提供动力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI for Audience Prediction and Profiling to Power Innovative TV Content Recommendation Services
In contemporary TV audience prediction, outliers are considered mere anomalies in the otherwise cyclical trend and seasonality components that can be used to make predictions. In the ReTV project, we want to provide more accurate audience predictions in order to enable innovative services for TV content recommendation. This paper presents a concept for identifying the source of outliers and factoring TV content categories and the occurrence of events as additional features for training TV audience prediction. We show how this can improve the accuracy of the audience prediction. Finally, we outline how this work could also be combined with AI-enabled audience profiling to power new content recommendation services.
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