发表机构
University of Queensland; Alibaba Group; Southeast University; Adelaide University（昆士兰大学; 阿里巴巴集团; 东南大学; 阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对静态模态融合无法适应查询差异的问题，提出AdaM-Rec，利用LLM和代理召回任务动态路由文本与视觉模态，提升多模态推荐性能。
AI 中文摘要
尽管最近的多模态推荐系统已证明结合视觉和文本信息能有效提升下游性能，但大多数现有方法依赖于静态模态融合，假设文本和视觉信号的相对重要性在推荐场景中保持稳定。这种设计可能无法充分考虑到推荐请求中的一个重要变化：某些查询需要细粒度的视觉线索，而另一些查询则更适合由文本或功能语义来处理，在这种情况下，不加区分的模态融合会引入无信息量的线索并损害推荐质量。为解决这一问题，我们提出了AdaM-Rec，一个基于LLM的多模态推荐自适应模态路由框架，能够针对用户特定查询动态校准对文本和多模态证据的依赖。基于项目和用户偏好的结构化自然语言表示，它通过代理召回任务估计模态可靠性。具体来说，它生成与实际查询粒度匹配的伪查询，同时指向用户正向交互的项目作为可验证的代理目标，评估在类似场景中哪种模态能产生更好的召回性能，并以智能体方式优化路由策略。然后，它使用优化后的策略执行路由召回，用协同项目丰富结果，并根据候选项目与查询和用户偏好的相关性进行排序。实验表明，AdaM-Rec在与最先进基线的对比中表现出强劲性能，凸显了在多模态推荐中对模态依赖进行自适应控制的有效性和更广泛的潜力。
英文摘要
While recent multimodal recommender systems have demonstrated the effectiveness of incorporating visual and textual information to improve downstream performance, most existing methods rely on static modality fusion, assuming that the relative importance of textual and visual signals remains stable across recommendation scenarios. This design may not fully account for an important variation across recommendation requests: some queries require fine-grained visual cues, whereas others are better served by textual or functional semantics, in which case indiscriminate modality fusion brings in uninformative cues and impairs recommendation quality. To address this, we propose AdaM-Rec, an LLM-based framework for adaptive modality routing in multimodal recommendation, which enables dynamic calibration of reliance on textual and multimodal evidence for user-specific queries. Built on structured natural-language representations of items and user preferences, it estimates modality reliability using proxy recall tasks. Specifically, it generates pseudo-queries that match the granularity of the actual query while pointing to the user's positively interacted items as verifiable proxy targets, evaluating which modality yields better recall performance in analogous scenarios and optimizing the routing strategy in an agentic manner. It then performs routed recall with optimized strategy, enriches results with collaborative items, and ranks candidates by their relevance to both the query and user preferences. Experiments demonstrate that AdaM-Rec delivers strong performance against state-of-the-art baselines, highlighting the effectiveness and broader potential of adaptive control over modality reliance in multimodal recommendation.
