Learning individual preferences from aggregate data: A genetic algorithm for discovering baskets of television shows with affinities to political and social interests

作者：

Highlights：

• A framework based on genetic and multi-objective evolutionary algorithms is proposed.

• The framework estimates user political preferences from unlabeled TV viewership data.

• The framework discovers sets of shows whose viewership determines social preferences.

• We apply and test the framework to Nielsen National Database on TV viewership in 2016.

• We examine politics, global warming, same-sex marriage, and abortion in the US.

摘要

•A framework based on genetic and multi-objective evolutionary algorithms is proposed.•The framework estimates user political preferences from unlabeled TV viewership data.•The framework discovers sets of shows whose viewership determines social preferences.•We apply and test the framework to Nielsen National Database on TV viewership in 2016.•We examine politics, global warming, same-sex marriage, and abortion in the US.

论文关键词：Genetic algorithms,Multi-objective evolutionary algorithms,Audience analytics,Aggregate data,TV panel data,Micro-targeting

论文评审过程：Received 22 March 2020, Revised 23 September 2020, Accepted 28 October 2020, Available online 3 November 2020, Version of Record 24 January 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114184