Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user’s context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user’s personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user’s context.

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Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System

In this paper, we shed light on explaining user models for transparent recommendation while considering user personal characteristics. To this end, we developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides interactive, layered explanations of the user model with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable recommender system with regard to different explanation goals. Based on the study results, we provided some suggestions to support the effective design of user model explanations for transparent recommendation.

Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User Study

The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the realm of scientific publication recommendations. In a content-based scientific publication RS, recommendations are composed by observing the features of users and papers. Content-based recommendation encompasses three primary steps, namely, item representation, user modeling, and recommendation generation. A crucial part of generating recommendations is the user modeling process. Nevertheless, this step is often neglected in existing content-based scientific publication RS. Moreover, most existing approaches do not capture the semantics of user models and papers. To address these limitations, in this paper we present a transparent Recommendation and Interest Modeling Application (RIMA), a content-based scientific publication RS that implicitly derives user interest models from their authored papers. To address the semantic issues, RIMA combines word embedding-based keyphrase extraction techniques with knowledge bases to generate semantically-enriched user interest models, and additionally leverages pretrained transformer sentence encoders to represent user models and papers and compute their similarities. The effectiveness of our approach was assessed through an offline evaluation by conducting extensive experiments on various datasets along with user study (N = 22), demonstrating that (a) combining SIFRank and SqueezeBERT as an embedding-based keyphrase extraction method with DBpedia as a knowledge base improved the quality of the user interest modeling step, and (b) using the msmarco-distilbert-base-tas-b sentence transformer model achieved better results in the recommendation generation step.

Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

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. 

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

Despite the vast amount of research on interactive recommender systems (RS) and explainable recommendation, there is a lack of research on how to incorporate interactivity features in explainable RS. To address this research gap, a possible solution to achieve interactive explanation could be to provide What if explanations that can help users iteratively build better mental models of how the RS works. Through an iterative human-centered design (HCD) process, we designed What if interactive explanations in the transparent Recommendation and Interest Modeling Application (RIMA) and explored how this type of explanation could impact the understandability of and interaction with an explainable RS. Our investigation showed that providing What if explanations has a positive effect on different aspects of a RS such as transparency, trust, user control, satisfaction

What if Interactive Explanation in a Scientific Literature Recommender System

In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines unsupervised keyword extraction algorithms, knowledge bases, and word embedding techniques to address the semantic issues in the interest modeling task.

SIMT: A Semantic Interest Modeling Toolkit

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SIMT: A Semantic Interest Modeling Toolkit