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.

翻译：视觉艺术（VA）推荐具有复杂性，因为它必须同时考虑用户（如博物馆参观者）和其他利益相关者（如博物馆策展人）的兴趣。我们研究了如何在VA推荐中有效纳入关键利益相关者，同时兼顾以用户为中心的评价指标，如新颖性、意外性和多样性。我们提出了MOSAIC，一种新颖的多模态多利益相关者感知方法，该方法采用最先进的CLIP和BLIP骨干架构，并设定两个联合优化目标：跨不同类别画作的流行度与代表性选择。我们基于213名用户提供的偏好数据进行了离线评估，随后对100名众包工作者开展了用户研究。研究发现，流行度具有显著影响，且受到用户的积极评价，而代表性的影响则微乎其微。MOSAIC的影响不仅限于参观者，还能惠及各类艺术利益相关者。其以用户为中心的方法具有更广泛的适用性，为需要兼顾多方利益相关者的跨领域内容推荐提供了进展。