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Open user models provide affordance for a transparent user control over recommendations based on shared symbolic representation within the system. Users must build their user profile by adding these symbols and tuning their importance to get meaningful recommendations. Since the link between these symbols and the reference explanation is often unavailable, it can be difficult for users to understand them. These symbols are often referred to as concepts, tags, areas, topics, labels, features, or keyphrases.

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This study showcases an information exploration system that helps students identify potential faculty members to collaborate with. The system works by matching user and faculty profiles that contain keywords or phrases representing topics/areas of interest. Students must develop their understanding of research topics while building their profiles, which can become challenging as they add more keywords.

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To support students in controlling the recommendation, we introduce post hoc explanations with three levels of detail: no explanations, individual explanation for topics, and explanation of the relationships between topics.

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[[File:umapadjunct24-81-fig2.jpg]]

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[[File:umapadjunct24-81-fig3.jpg]]

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== Publications ==

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* Rully Agus Hendrawan, Peter Brusilovsky, Arun Balajiee Lekshmi Narayanan, and Jordan Barria-Pineda. 2024. Explanations in Open User Models for Personalized Information Exploration. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct '24). Association for Computing Machinery, New York, NY, USA, 256–263. ([https://doi.org/10.1145/3631700.3665188 paper])

Grapevine2 - Revision history

Rah225 at 18:26, 4 March 2026

Arl122: Created blank page

← Older revision	Revision as of 18:26, 4 March 2026
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	+	Open user models provide affordance for a transparent user control over recommendations based on shared symbolic representation within the system. Users must build their user profile by adding these symbols and tuning their importance to get meaningful recommendations. Since the link between these symbols and the reference explanation is often unavailable, it can be difficult for users to understand them. These symbols are often referred to as concepts, tags, areas, topics, labels, features, or keyphrases.

	+	This study showcases an information exploration system that helps students identify potential faculty members to collaborate with. The system works by matching user and faculty profiles that contain keywords or phrases representing topics/areas of interest. Students must develop their understanding of research topics while building their profiles, which can become challenging as they add more keywords.
	+
	+	To support students in controlling the recommendation, we introduce post hoc explanations with three levels of detail: no explanations, individual explanation for topics, and explanation of the relationships between topics.
	+
	+	[[File:umapadjunct24-81-fig2.jpg]]
	+
	+
	+	[[File:umapadjunct24-81-fig3.jpg]]
	+
	+
	+	== Publications ==
	+
	+	* Rully Agus Hendrawan, Peter Brusilovsky, Arun Balajiee Lekshmi Narayanan, and Jordan Barria-Pineda. 2024. Explanations in Open User Models for Personalized Information Exploration. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct '24). Association for Computing Machinery, New York, NY, USA, 256–263. ([https://doi.org/10.1145/3631700.3665188 paper])