Visualization for Recommendation Explainability: A Survey and New Perspectives

Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page

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

Explainable 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.Comment: 23 page

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach

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

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

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach