# Joint Neural Collaborative Filtering for Recommender Systems

arXiv: https://arxiv.org/abs/1907.03459 · HTML: https://mlsanity.com/paper/1907.03459/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

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

- [Membership Inference Attacks Against Recommender Systems](https://mlsanity.com/paper/2109.08045/) (2021)
- [Graph Meta Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.03969/) (2021)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [Efficient Data-specific Model Search for Collaborative Filtering](https://mlsanity.com/paper/2106.07453/) (2021)
- [Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering](https://mlsanity.com/paper/2307.11004/) (2023)
- [Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems](https://mlsanity.com/paper/2201.03158/) (2022)
- [Dual-embedding based Neural Collaborative Filtering for Recommender Systems](https://mlsanity.com/paper/2102.02549/) (2021)
- [Capability-Aware Early-Stage Research Idea Evaluation](https://mlsanity.com/paper/2601.12473/) (2026)
- [Ae2I: A Double Autoencoder for Imputation of Missing Values](https://mlsanity.com/paper/2301.06633/) (2023)
- [Predicting Movie Hits Before They Happen with LLMs](https://mlsanity.com/paper/2505.02693/) (2025)
- [VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents](https://mlsanity.com/paper/2609.38270/) (2026)

## Abstract (arXiv)

We propose a J-NCF method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization, which takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-word datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.