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.
| Original language | English |
|---|---|
| Title of host publication | ACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 241–245 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781450367110 |
| ISBN (Print) | 9781450367110 |
| DOIs | |
| Publication status | Published - 6 Jun 2019 |
| Externally published | Yes |
Keywords
- Explainability
- News recommender systems
- Self-actualization
- User control
- User profile