{"title":"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","authors":"L. Nixon, K. Ciesielski, Basil Philipp","doi":"10.1145/3347449.3357485","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":276496,"journal":{"name":"Proceedings of the 1st International Workshop on AI for Smart TV Content Production, Access and Delivery","volume":"85-86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"AI for Audience Prediction and Profiling to Power Innovative TV Content Recommendation Services\",\"authors\":\"L. Nixon, K. Ciesielski, Basil Philipp\",\"doi\":\"10.1145/3347449.3357485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":276496,\"journal\":{\"name\":\"Proceedings of the 1st International Workshop on AI for Smart TV Content Production, Access and Delivery\",\"volume\":\"85-86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1st International Workshop on AI for Smart TV Content Production, Access and Delivery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3347449.3357485\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st International Workshop on AI for Smart TV Content Production, Access and Delivery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3347449.3357485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.