Multimodal recommender systems (RSs) represent items in the catalog through multimodal data (e.g., product images and descriptions) that, in some cases, might be noisy or (even worse) missing. In those scenarios, the common practice is to drop items with missing modalities and train the multimodal RSs on a subsample of the original dataset. To date, the problem of missing modalities in multimodal recommendation has still received limited attention in the literature, lacking a precise formalisation as done with missing information in traditional machine learning. In this work, we first provide a problem formalisation for missing modalities in multimodal recommendation. Second, by leveraging the user-item graph structure, we re-cast the problem of missing multimodal information as a problem of graph features interpolation on the item-item co-purchase graph. On this basis, we propose four training-free approaches that propagate the available multimodal features throughout the item-item graph to impute the missing features. Extensive experiments on popular multimodal recommendation datasets demonstrate that our solutions can be seamlessly plugged into any existing multimodal RS and benchmarking framework while still preserving (or even widen) the performance gap between multimodal and traditional RSs. Moreover, we show that our graph-based techniques can perform better than traditional imputations in machine learning under different missing modalities settings. Finally, we analyse (for the first time in multimodal RSs) how feature homophily calculated on the item-item graph can influence our graph-based imputations.

翻译：多模态推荐系统通过多模态数据（例如产品图像和描述）表示目录中的物品，这些数据在某些情况下可能存在噪声甚至缺失。在此类场景中，常规做法是丢弃具有缺失模态的物品，并在原始数据集的子样本上训练多模态推荐系统。迄今为止，多模态推荐中的缺失模态问题在文献中仍未获得足够重视，缺乏像传统机器学习中缺失信息处理那样的精确定义。在本研究中，我们首先对多模态推荐中的缺失模态问题进行了形式化定义。其次，通过利用用户-物品图结构，我们将多模态信息缺失问题重新定义为物品-物品共购图上的图特征插值问题。在此基础上，我们提出了四种无需训练的方法，通过物品-物品图传播可用的多模态特征以补全缺失特征。在主流多模态推荐数据集上的大量实验表明，我们的解决方案可以无缝集成到任何现有的多模态推荐系统及基准框架中，同时保持（甚至扩大）多模态推荐系统与传统推荐系统之间的性能差距。此外，我们证明在不同缺失模态设置下，基于图的技术能够优于传统的机器学习补全方法。最后，我们首次在多模态推荐系统中分析了物品-物品图上计算的特征同质性如何影响基于图的补全效果。