A content-based fashion recommender that fuses visual similarity, synthetic popularity, and hand-defined category similarity achieves modest self-reported accuracy on a self-simulated benchmark.
MOSAIC: Multimodal Multistakeholder-aware Visual Art Recommendation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
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.
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
A content-based fashion recommender that fuses visual similarity, synthetic popularity, and hand-defined category similarity achieves modest self-reported accuracy on a self-simulated benchmark.