A Live-User Study of Opinionated Explanations for Recommender Systems

By Khalil Muhammad, Aonghus Lawlor, Barry Smyth. 2016

50 citations recorded by the source corpus.

This paper describes an approach for generating rich and compelling explanations in recommender systems, based on opinions mined from user-generated reviews. The explanations highlight the features of a recommended item that matter most to the user and also relate them to other recommendation alternatives and the user's past activities to provide a context.

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

1. Ingrid Nunes
2. S. S. Iyengar
3. Dietmar Jannach
4. Xiaolong Zhu
5. Tao Li
6. Ramesh Baral
7. Xu Chen
8. Yongfeng Zhang

In Information Systems

1. Ingrid Nunes
2. S. S. Iyengar
3. Dietmar Jannach
4. Xiaolong Zhu
5. Tao Li
6. Ramesh Baral
7. Yongfeng Zhang
8. Xu Chen

Citations in the corpus

• ReEL: Review Aware Explanation of Location Recommendation (2018, result): “Contrary to that, some of the studies [17, 18, 35, 36, 40, 41, 43, 57] have already claimed the user persuasiveness due to explainability in real-world systems.”
• A systematic review and taxonomy of explanations in decision support and recommender systems (2017, background): “Usefulness Decisive features (Muhammad et al. 2016) Match score + decisive features (Zanker and Ninaus 2010) Match score + decisive features (Zanker 2012)”