  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.
