# LRMM: Learning to Recommend with Missing Modalities

arXiv: https://arxiv.org/abs/1808.06791 · HTML: https://mlsanity.com/paper/1808.06791/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [Exploring Missing Modality in Multimodal Egocentric Datasets](https://mlsanity.com/paper/2401.11470/) (2024)
- [Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios](https://mlsanity.com/paper/2504.16352/) (2025)
- [Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild](https://mlsanity.com/paper/2107.09510/) (2021)
- [Unsupervised domain adaptation with non-stochastic missing data](https://mlsanity.com/paper/2109.09505/) (2021)
- [Benchmarking Multimodal Large Language Models for Missing Modality Completion in Product Catalogues](https://mlsanity.com/paper/2601.19750/) (2026)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [Meta-Modal Agent: Sequential Evidence Routing for Missing-Modality Candidate Reranking](https://mlsanity.com/paper/2605.25007/) (2026)
- [HyperAgent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation](https://mlsanity.com/paper/2608.01846/) (2026)

## Abstract (arXiv)

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.