# Neural Attentive Multiview Machines

arXiv: https://arxiv.org/abs/2002.07696 · HTML: https://mlsanity.com/paper/2002.07696/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps](https://mlsanity.com/paper/2204.11073/) (2022)
- [Sliding Spectrum Decomposition for Diversified Recommendation](https://mlsanity.com/paper/2107.05204/) (2021)
- [Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference](https://mlsanity.com/paper/2106.01186/) (2021)
- [Deep Integrated Explanations](https://mlsanity.com/paper/2310.15368/) (2023)
- [Interpreting BERT-based Text Similarity via Activation and Saliency Maps](https://mlsanity.com/paper/2208.06612/) (2022)
- [Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates](https://mlsanity.com/paper/2112.07615/) (2021)
- [Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models](https://mlsanity.com/paper/2310.16584/) (2023)
- [MetricBERT: Text Representation Learning via Self-Supervised Triplet Training](https://mlsanity.com/paper/2208.06610/) (2022)
- [Representation Learning via Variational Bayesian Networks](https://mlsanity.com/paper/2306.16326/) (2023)
- [Fidelity-Aware Recommendation Explanations via Stochastic Path Integration](https://mlsanity.com/paper/2511.18047/) (2025)
- [Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems](https://mlsanity.com/paper/2511.18024/) (2025)

## Abstract (arXiv)

An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Neural Attentive Multiview machine that learns multiview item representations and similarity by employing a novel attention mechanism. NAM harnesses multiple information sources and automatically quantifies their relevancy with respect to a supervised task. Finally, a very practical advantage of NAM is its robustness to the case of dataset with missing views. We demonstrate the effectiveness of NAM for the task of movies and app recommendations. Our evaluations indicate that NAM outperforms single view models as well as alternative multiview methods on item recommendations tasks, including cold-start scenarios.