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Multimodal learning has shown promising performance in content-basedrecommendation due to the auxiliary user and item information of multiplemodalities such as text and images. However, the problem of incomplete andmissing modality is rarely explored and most existing methods fail in learninga recommendation model with missing or corrupted modalities. In this paper, wepropose LRMM, a novel framework that mitigates not only the problem of missingmodalities but also more generally the cold-start problem of recommendersystems. We propose modality dropout (m-drop) and a multimodal sequentialautoencoder (m-auto) to learn multimodal representations for complementing andimputing missing modalities. Extensive experiments on real-world Amazon datashow that LRMM achieves state-of-the-art performance on rating predictiontasks. More importantly, LRMM is more robust to previous methods in alleviatingdata-sparsity and the cold-start problem.

LRMM: Learning to Recommend with Missing Modalities