# Sparsity Regularization For Cold-Start Recommendation

arXiv: https://arxiv.org/abs/2201.10711 · HTML: https://mlsanity.com/paper/2201.10711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models](https://mlsanity.com/paper/1705.10513/) (2017)
- [From Zero-Shot Learning to Cold-Start Recommendation](https://mlsanity.com/paper/1906.08511/) (2019)
- [Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems](https://mlsanity.com/paper/1705.00105/) (2017)

## Abstract (arXiv)

Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. In this paper we introduce a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. Our system models user purchase behavior using weighted user-product preferences (explicit feedback) rather than binary user-product interactions (implicit feedback). Using this we develop a novel sparse adversarial model, SRLGAN, for Cold-Start Recommendation leveraging the sparse user-purchase behavior which ensures training stability and avoids over-fitting on warm users. We evaluate the SRLGAN on two popular datasets and demonstrate state-of-the-art results.