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MOSAIC: multimodal multistakeholder-aware visual art recommendation.

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Title: MOSAIC: multimodal multistakeholder-aware visual art recommendation.
Authors: Yilma, Bereket A.1 (AUTHOR) bereket.yilma@uni.lu, Leiva, Luis A.1 (AUTHOR) luis.leiva@uni.lu
Source: User Modeling & User-Adapted Interaction. Dec2025, Vol. 35 Issue 4, p1-33. 33p.
Subjects: Art, Recommender systems, Stakeholder theory, Evaluation methodology, Heterogeneity, Stakeholder analysis, Multimodal user interfaces, Mosaics (Art)
Abstract: Visual art (VA) recommendation is complex, as it has to consider the interests of users (e.g. museum visitors) and other stakeholders (e.g. museum curators). We study how to effectively account for key stakeholders in VA recommendations while also considering user-centred measures such as novelty, serendipity, and diversity. We propose MOSAIC, a novel multimodal multistakeholder-aware approach using state-of-the-art CLIP and BLIP backbone architectures and two joint optimisation objectives: popularity and representative selection of paintings across different categories. We conducted an offline evaluation using preferences elicited from 213 users followed by a user study with 100 crowdworkers. We found a strong effect of popularity, which was positively perceived by users, and a minimal effect of representativeness. MOSAIC's impact extends beyond visitors, benefiting various art stakeholders. Its user-centric approach has broader applicability, offering advancements for content recommendation across domains that require considering multiple stakeholders. [ABSTRACT FROM AUTHOR]
Copyright of User Modeling & User-Adapted Interaction is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: MOSAIC: multimodal multistakeholder-aware visual art recommendation.
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  Data: <searchLink fieldCode="AR" term="%22Yilma%2C+Bereket+A%2E%22">Yilma, Bereket A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bereket.yilma@uni.lu</i><br /><searchLink fieldCode="AR" term="%22Leiva%2C+Luis+A%2E%22">Leiva, Luis A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> luis.leiva@uni.lu</i>
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  Data: <searchLink fieldCode="JN" term="%22User+Modeling+%26+User-Adapted+Interaction%22">User Modeling & User-Adapted Interaction</searchLink>. Dec2025, Vol. 35 Issue 4, p1-33. 33p.
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  Data: <searchLink fieldCode="DE" term="%22Art%22">Art</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Stakeholder+theory%22">Stakeholder theory</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneity%22">Heterogeneity</searchLink><br /><searchLink fieldCode="DE" term="%22Stakeholder+analysis%22">Stakeholder analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Multimodal+user+interfaces%22">Multimodal user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Mosaics+%28Art%29%22">Mosaics (Art)</searchLink>
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  Label: Abstract
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  Data: Visual art (VA) recommendation is complex, as it has to consider the interests of users (e.g. museum visitors) and other stakeholders (e.g. museum curators). We study how to effectively account for key stakeholders in VA recommendations while also considering user-centred measures such as novelty, serendipity, and diversity. We propose MOSAIC, a novel multimodal multistakeholder-aware approach using state-of-the-art CLIP and BLIP backbone architectures and two joint optimisation objectives: popularity and representative selection of paintings across different categories. We conducted an offline evaluation using preferences elicited from 213 users followed by a user study with 100 crowdworkers. We found a strong effect of popularity, which was positively perceived by users, and a minimal effect of representativeness. MOSAIC's impact extends beyond visitors, benefiting various art stakeholders. Its user-centric approach has broader applicability, offering advancements for content recommendation across domains that require considering multiple stakeholders. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of User Modeling & User-Adapted Interaction is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188316883
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        Value: 10.1007/s11257-025-09435-3
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      – Code: eng
        Text: English
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      – SubjectFull: Art
        Type: general
      – SubjectFull: Recommender systems
        Type: general
      – SubjectFull: Stakeholder theory
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      – SubjectFull: Evaluation methodology
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      – SubjectFull: Heterogeneity
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      – SubjectFull: Multimodal user interfaces
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      – SubjectFull: Mosaics (Art)
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              M: 12
              Text: Dec2025
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              Y: 2025
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