Congratulations to Qing Li, Sharon Lynn Chu, Nanjie Rao, and Mahsan Nourani, the authors of Understanding the Effects of Explanation Types and User Motivations on Movie Recommender System Use for their full paper submission being accepted to the eighth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020)! This is Qing’s first paper accepted as first author, great work Qing and her co-authors on this accomplishment!
The paper abstract can be found below:
It is becoming increasingly common for intelligent systems, such as recommender systems, to provide explanations for their generated recommendations to 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 the 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; and 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.