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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

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.

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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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Details

Item Type: Conference or Workshop Item (Paper)
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Tsai, CHcht77@pitt.eduCHT770000-0001-9188-0362
Brusilovsky, Peterpeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 26 September 2020
Event Title: 2020 ACM Conference on Recommender Systems (RecSys’20)
Event Type: Conference
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Date Deposited: 15 Mar 2021 15:09
Last Modified: 11 Jun 2024 18:55
URI: http://d-scholarship.pitt.edu/id/eprint/40345

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