Understanding the Effects of Explanation Types and User Motivations on Recommender System Use

Authors

  • Qing Li Santa Fe College
  • Sharon Lynn Chu University of Florida, Gainesville
  • Nanjie Rao University of Florida, Gainesville
  • Mahsan Nourani University of Florida

Abstract

It is becoming increasingly common for intelligent systems, such as recommender systems, to provide explanations for their generated recommendations to the users. However, we still do not have a good understanding of what types of explanations work and what factors affect the effectiveness of different types of explanations. Our work focuses on explanations for movie recommender systems. This paper presents a mixed study where we hypothesize that the type of explanation, as well as user motivation for watching movies, will affect how users respond to recommendation system explanations. Our study compares three types of explanations: i) neighbor-ratings, ii) profile-based, and iii) event-based, as well as three types of user movie-watching motivations: i) hedonic (fun and relaxation), ii) eudaimonic (inspiration and meaningfulness), and iii) educational (learning new content). We discuss the implications of the study results for the design of explanations for movie recommender systems, and future novel research directions that the study results uncover.

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Published

2020-10-01

How to Cite

Li, Q., Chu, S., Rao, N., & Nourani, M. (2020). Understanding the Effects of Explanation Types and User Motivations on Recommender System Use. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1), 83-91. Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/7466