Tsai, CH and Brusilovsky, Peter
(2020)
User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis. In: Proceedings of Joint Workshop on Interfaces and Human Decision Making for Recommender Systems.
In: 2020 ACM Conference on Recommender Systems (RecSys’20), Online.
Abstract
Controllable and explainable intelligent user interfaces have been used to provide transparent recommendations. Many researchers have explored interfaces that support user control and provide explanations of the recommendation process and models. To extend the works to real-world decision-making scenarios, we need to understand further the users' mental models of the enhanced system components. In this paper, we make a step in this direction by investigating a free form feedback left by users of social recommender systems to specify the reasons of selecting prompted social recommendations. With a user study involving 50 subjects (N=50), we present the linguistic changes in using controllable and explainable interfaces for a social information-seeking task. Based on our findings, we discuss design implications for controllable and explainable recommender systems.
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