Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System

AbstractExplainable recommender systems (RS) have traditionally followed a one-size-fits-all approach, delivering the same explanation level of detail to each user, without considering their individual needs and goals. Further, explanations in RS have so far been presented mostly in a static and non-interactive manner. To fill these research gaps, we aim in this paper to adopt a user-centered, interactive explanation model that provides explanations with different levels of detail and empowers users to interact with, control, and personalize the explanations based on their needs and preferences. We followed a user-centered approach to design interactive explanations with three levels of detail (basic, intermediate, and advanced) and implemented them in the transparent Recommendation and Interest Modeling Application (RIMA). We conducted a qualitative user study (N = 14) to investigate the impact of providing interactive explanations with varying level of details on the users' perception of the explainable RS. Our study showed qualitative evidence that fostering interaction and giving users control in deciding which explanation they would like to see can meet the demands of users with different needs, preferences, and goals, and consequently can have positive effects on different crucial aspects in explainable recommendation, including transparency, trust, satisfaction, and user experience.Keywords: Recommender systemintelligent user interfacesexplainable recommendationinteractive explanationintelligibility typesvisualization Authors contributionsConceptualization, M.G., M.A.C. and T.V.; Methodology, M.G. and M.A.C.; Validation, M.A.C.; Software, T.V. and S.J.; Writing–original draft preparation, M.G. and T.V.; Writing–review and editing, M.G. and M.A.C.; Visualization, M.G., T.V., S.J., Q.U.A, R.A. and C.S.; Supervision, M.A.C. All authors have read and agreed to the published version of the manuscript.Institutional review board statementThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Department of Computer Science and Applied Cognitive Science of the Faculty of Engineering at the University of Duisburg-Essen.Informed consent statementInformed consent was obtained from all subjects involved in the study.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe interview data presented in this study are not publicly available due to ethical and privacy restrictions.Notes1 https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b2 https://www.soscisurvey.deAdditional informationFundingThe authors thank the German Research Foundation (DFG) for partly funding this work under grant No. GRK 2167, Research Training Group "User-Centred Social Media".Notes on contributorsMouadh GuesmiMouadh Guesmi is a research assistant and PhD student in the Social Computing Group, Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen, Germany. His research interests include human-computer interaction, user modeling and personalization, explainable recommender systems, and information visualization.Mohamed Amine ChattiMohamed Amine Chatti is a professor of computer science and head of the Social Computing Group in the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen, Germany. His research focuses on human-centered learning analytics, personalization, recommender systems, trustworthy human-AI interaction, and intelligent explanation interfaces.Shoeb JoarderShoeb Joarder is a research assistant and PhD student in the Social Computing Group, Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen, Germany. His main research areas involve human-centered learning analytics, visual analytics, and big data.Qurat Ul AinQurat Ul Ain is a research assistant and PhD student in the Social Computing Group, Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen, Germany. Her main research interests include human-computer interaction and explainable recommender systems.Rawaa AlatrashRawaa Alatrash is a research assistant and PhD student in the Social Computing Group, Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen, Germany. Her research interests include recommender systems, natural language processing, graph neural networks, and user modeling.Clara SiepmannClara Siepmann is a research assistant and PhD student in the Social Computing Group, Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen, Germany. Her research interests include human-computer interaction, explainable recommender systems, and the relationship between trust and transparency in intelligent systems.Tannaz VahidiTannaz Vahidi has a Master's degree in computer engineering from the Faculty of Engineering at the University of Duisburg-Essen, Germany.