# TransNets: Learning to Transform for Recommendation

arXiv: https://arxiv.org/abs/1704.02298 · HTML: https://mlsanity.com/paper/1704.02298/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2017.

## Built on by (most cited first, 13 of 13)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Enhancing Content-based Recommendation via Large Language Model](https://mlsanity.com/paper/2404.00236/) (2024)
- [Augmenting the User-Item Graph with Textual Similarity Models](https://mlsanity.com/paper/2109.09358/) (2021)
- [Dynamic Review-based Recommenders](https://mlsanity.com/paper/2110.14747/) (2021)
- [Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation](https://mlsanity.com/paper/2110.12648/) (2021)
- [SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation](https://mlsanity.com/paper/2108.08022/) (2021)
- [Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation](https://mlsanity.com/paper/2110.10358/) (2021)
- [Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency](https://mlsanity.com/paper/2205.01759/) (2022)
- [Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation](https://mlsanity.com/paper/2205.06296/) (2022)
- [Opinion mining using Double Channel CNN for Recommender System](https://mlsanity.com/paper/2307.07798/) (2023)
- [Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System](https://mlsanity.com/paper/2403.12096/) (2024)
- [Semantic-Enhanced Relational Metric Learning for Recommender Systems](https://mlsanity.com/paper/2406.10246/) (2024)
- [Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search](https://mlsanity.com/paper/2607.01387/) (2026)

## Abstract (arXiv)

Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.