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Open, Scrutable and Explainable Interest Models for Transparent Recommendation.

, , , , , , , and . IUI Workshops, volume 2903 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)

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The LAVA Model: Learning Analytics Meets Visual Analytics., , , and . CoRR, (2023)How to Design Effective Learning Analytics Indicators? A Human-Centered Design Approach., , , , , , and . EC-TEL, volume 12315 of Lecture Notes in Computer Science, page 303-317. Springer, (2020)Supporting Indicator Personalization and Platform Extensibility in Open Learning Analytics., , and . Technol. Knowl. Learn., 27 (2): 429-448 (2022)SIMT: A Semantic Interest Modeling Toolkit., , , , , and . UMAP (Adjunct Publication), page 75-78. ACM, (2021)Input or Output: Effects of Explanation Focus on the Perception of Explainable Recommendation with Varying Level of Details., , , , , , , , , and . IntRS@RecSys, volume 2948 of CEUR Workshop Proceedings, page 55-72. CEUR-WS.org, (2021)Visualization for Recommendation Explainability: A Survey and New Perspectives., , and . CoRR, (2023)Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System., , , , , , and . UMAP, page 254-264. ACM, (2022)Open, Scrutable and Explainable Interest Models for Transparent Recommendation., , , , , , , and . IUI Workshops, volume 2903 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User Study., , , , , , and . UMAP (Adjunct Publication), page 175-183. ACM, (2022)On-demand Personalized Explanation for Transparent Recommendation., , , , , , , and . UMAP (Adjunct Publication), page 246-252. ACM, (2021)