The effects of controllability and explainability in a social recommender system

In recent years, researchers in the field of recommender systems have explored a range of advanced interfaces to improve user interactions with recommender systems. Some of the major research ideas explored in this new area include the explainability and controllability of recommendations. Controllability enables end users to participate in the recommendation process by providing various kinds of input. Explainability focuses on making the recommendation process and the reasons behind specific recommendation more clear to the users. While each of these approaches contributes to making traditional “black-box” recommendation more attractive and acceptable to end users, little is known about how these approaches work together. In this paper, we investigate the effects of adding user control and visual explanations in a specific context of an interactive hybrid social recommender system. We present Relevance Tuner+, a hybrid recommender system that allows the users to control the fusion of multiple recommender sources while also offering explanations of both the fusion process and each of the source recommendations. We also report the results of a controlled study (N = 50) that explores the impact of controllability and explainability in this context.