{"title":"公共服务机构中数据驱动的建议","authors":"A. Piscopo, Maria Panteli, D. Penna","doi":"10.1145/3345002.3349286","DOIUrl":null,"url":null,"abstract":"The BBC is one of the world's leading broadcasters, producing a large amount of content for different audiences. Data-driven recommendations are a successful approach to increase user engagement providing tailored content and personalising their experience. However, concerns have been raised with regards to their effects on diversity and reinforcement of existing bias. Addressing these concerns is especially important for the BBC, whose values include trust, diversity, and impartiality. This position paper lays out the strategy followed by the BBC to develop automated recommendation systems, presenting our approach to create accurate, fair, and responsible recommendation systems.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Data-Driven Recommendations in a Public Service Organisation\",\"authors\":\"A. Piscopo, Maria Panteli, D. Penna\",\"doi\":\"10.1145/3345002.3349286\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The BBC is one of the world's leading broadcasters, producing a large amount of content for different audiences. Data-driven recommendations are a successful approach to increase user engagement providing tailored content and personalising their experience. However, concerns have been raised with regards to their effects on diversity and reinforcement of existing bias. Addressing these concerns is especially important for the BBC, whose values include trust, diversity, and impartiality. This position paper lays out the strategy followed by the BBC to develop automated recommendation systems, presenting our approach to create accurate, fair, and responsible recommendation systems.\",\"PeriodicalId\":153835,\"journal\":{\"name\":\"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3345002.3349286\",\"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 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3345002.3349286","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data-Driven Recommendations in a Public Service Organisation
The BBC is one of the world's leading broadcasters, producing a large amount of content for different audiences. Data-driven recommendations are a successful approach to increase user engagement providing tailored content and personalising their experience. However, concerns have been raised with regards to their effects on diversity and reinforcement of existing bias. Addressing these concerns is especially important for the BBC, whose values include trust, diversity, and impartiality. This position paper lays out the strategy followed by the BBC to develop automated recommendation systems, presenting our approach to create accurate, fair, and responsible recommendation systems.