# Single-Branch Network Architectures to Close the Modality Gap in Multimodal Recommendation

arXiv: https://arxiv.org/abs/2509.18807 · HTML: https://mlsanity.com/paper/2509.18807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan](https://mlsanity.com/paper/2603.24569/) (2026)
- [SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions](https://mlsanity.com/paper/2605.11799/) (2026)
- [Learning Speaker Identity Beyond Language and Modality Constraints: Insights from the POLY-SIM 2026 Challenge](https://mlsanity.com/paper/2607.13669/) (2026)

## Abstract (arXiv)

Traditional recommender systems rely on collaborative filtering, using past user-item interactions to help users discover new items in a vast collection. In cold start, i.e., when interaction histories of users or items are not available, content-based recommender systems use side information instead. Hybrid recommender systems (HRSs) often employ multimodal learning to combine collaborative and side information, which we jointly refer to as modalities. Though HRSs can provide recommendations when some modalities are missing, their quality degrades. In this work, we utilize single-branch neural networks equipped with weight sharing, modality sampling, and contrastive loss to provide accurate recommendations even in missing modality scenarios by narrowing the modality gap. We compare these networks with multi-branch alternatives and conduct extensive experiments on three datasets. Six accuracy-based and four beyond-accuracy-based metrics help assess the recommendation quality for the different training paradigms and their hyperparameters in warm-start and missing modality scenarios. We quantitatively and qualitatively study the effects of these different aspects on bridging the modality gap. Our results show that single-branch networks achieve competitive performance in warm-start scenarios and are significantly better in missing modality settings. Moreover, our approach leads to closer proximity of an item's modalities in the embedding space. Our full experimental setup is available at https://github.com/hcai-mms/single-branch-networks.