Emily Sullivan, D. Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, M. Makhortykh, Domokos M. Kelen, Daricia Wilkinson, David Graus, N. Tintarev
{"title":"带着目的阅读新闻:解释自我实现的用户配置文件","authors":"Emily Sullivan, D. Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, M. Makhortykh, Domokos M. Kelen, Daricia Wilkinson, David Graus, N. Tintarev","doi":"10.1145/3314183.3323456","DOIUrl":null,"url":null,"abstract":"Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their 'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Reading News with a Purpose: Explaining User Profiles for Self-Actualization\",\"authors\":\"Emily Sullivan, D. Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, M. Makhortykh, Domokos M. Kelen, Daricia Wilkinson, David Graus, N. Tintarev\",\"doi\":\"10.1145/3314183.3323456\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their 'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.\",\"PeriodicalId\":240482,\"journal\":{\"name\":\"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization\",\"volume\":\"121 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3314183.3323456\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3314183.3323456","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reading News with a Purpose: Explaining User Profiles for Self-Actualization
Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their 'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.