DOI

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.

Original languageEnglish
Title of host publicationUMAP'19 Adjunct
Subtitle of host publicationAdjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages241-245
Number of pages5
ISBN (Print)978-1-4503-6711-0
DOIs
Publication statusPublished - 2019
Event27th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2019 - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019

Conference

Conference27th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2019
CountryCyprus
CityLarnaca
Period9/06/1912/06/19

    Research areas

  • Explainability, News recommender systems, Self-actualization, User control, User profile

ID: 55480661