Do News Consumers Want Explanations for Personalized News Rankings
By Maartje ter Hoeve, Mathieu Heruer, Daan Odijk, Anne Schuth, Martijn Spitters, Maarten de Rijke. 2017
33 citations recorded by the source corpus.
To gain more insight in the question whether personalized news recommender systems should be responsible for their recommendations and transparent about their decisions, we study whether news consumers want explanations of why these news articles are recommended to them and what they find the best way to explain this. We survey users of Blendle’s news recommendation system, and from 120 respondents we learn that news consumers do want explanations, yet do not have a very strong preference for how explanations should be shown to them. Moreover, we perform an A/B test that shows that the open rate per user does not change if users are provided with reasons for the articles recommended for them. Most likely this is because users did not pay attention to the reasons.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Reuben Binns
- Michael Veale
- Maarten de Rijke
- Nigel R. Shadbolt
- M. V. Kleek
- Ana Lucic
- Hinda Haned
- Jennifer Wortman Vaughan
In Information Systems
- Reuben Binns
- Maarten de Rijke
- Nigel R. Shadbolt
- Michael Veale
- Masrour Zoghi
- Shimon Whiteson
- Michael Schmidt
- Ruhi Sarikaya
Citations in the corpus
- Explaining Predictions from Tree-based Boosting Ensembles (2019, background): “Understanding how “black-box” models arrive at their predictions has sparked significant interest from both within and outside the IR community. can be in the context of rankings [11], recommendations [12], or digital assistants that engage in interactive question answering [8].”
- 'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “However, a reported need for explanation may not always correspond to differences in behaviour or performance; in a study of news recommender systems, end-users expressed a desire for explanations, but the number of news items they opened did not change when provided with reasons for their recommend”