Empowered by semantic-rich content information, multimedia recommendation has emerged as a potent personalized technique. Current endeavors center around harnessing multimedia content to refine item representation or uncovering latent item-item structures based on modality similarity. Despite the effectiveness, we posit that these methods are usually suboptimal due to the introduction of irrelevant multimedia features into recommendation tasks. This stems from the fact that generic multimedia feature extractors, while well-designed for domain-specific tasks, can inadvertently introduce task-irrelevant features, leading to potential misguidance of recommenders. In this work, we propose a denoised multimedia recommendation paradigm via the Information Bottleneck principle (IB). Specifically, we propose a novel Information Bottleneck denoised Multimedia Recommendation (IBMRec) model to tackle the irrelevant feature issue. IBMRec removes task-irrelevant features from both feature and item-item structure perspectives, which are implemented by two-level IB learning modules: feature-level (FIB) and graph-level (GIB). In particular, FIB focuses on learning the minimal yet sufficient multimedia features. This is achieved by maximizing the mutual information between multimedia representation and recommendation tasks, while concurrently minimizing it between multimedia representation and pre-trained multimedia features. Furthermore, GIB is designed to learn the robust item-item graph structure, it refines the item-item graph based on preference affinity, then minimizes the mutual information between the original graph and the refined one. Extensive experiments across three benchmarks validate the effectiveness of our proposed model, showcasing high performance, and applicability to various multimedia recommenders.

翻译：借助语义丰富的内容信息，多媒体推荐已成为一种有效的个性化技术。当前研究主要围绕利用多媒体内容优化物品表征，或基于模态相似性挖掘潜在的物品间结构。尽管这些方法有效，我们认为它们通常并非最优，因为会将无关的多媒体特征引入推荐任务。这源于通用多媒体特征提取器虽然为特定领域任务精心设计，却可能无意中引入任务无关特征，从而误导推荐系统。本研究提出一种基于信息瓶颈原则的去噪多媒体推荐范式。具体而言，我们提出新颖的信息瓶颈去噪多媒体推荐模型以解决无关特征问题。该模型从特征和物品间结构两个层面去除任务无关特征，通过两级信息瓶颈学习模块实现：特征级与图级。其中，特征级模块专注于学习最小且充分的多媒体特征，通过最大化多媒体表征与推荐任务间的互信息，同时最小化其与预训练多媒体特征间的互信息来实现。此外，图级模块旨在学习鲁棒的物品间图结构，基于偏好亲和度优化物品关系图，并最小化原始图与优化图间的互信息。在三个基准数据集上的大量实验验证了所提模型的有效性，展现出优越性能及对多种多媒体推荐器的适用性。