# Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels

arXiv: https://arxiv.org/abs/1907.12366 · HTML: https://mlsanity.com/paper/1907.12366/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)
- [Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes](https://mlsanity.com/paper/2112.10558/) (2021)
- [Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings](https://mlsanity.com/paper/2401.15713/) (2024)
- [Autoencoder-based prediction of ICU clinical codes](https://mlsanity.com/paper/2305.04992/) (2023)
- [Citation Recommendation on Scholarly Legal Articles](https://mlsanity.com/paper/2311.05902/) (2023)

## Abstract (arXiv)

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.