# Convolutional Transformer Neural Collaborative Filtering

arXiv: https://arxiv.org/abs/2412.01376 · HTML: https://mlsanity.com/paper/2412.01376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 2 Dec 2024.

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

- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks](https://mlsanity.com/paper/2403.14302/) (2024)
- [GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model](https://mlsanity.com/paper/2209.07857/) (2022)
- [Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss](https://mlsanity.com/paper/2310.18700/) (2023)

## Abstract (arXiv)

In this study, we introduce Convolutional Transformer Neural Collaborative Filtering (CTNCF), a novel approach aimed at enhancing recommendation systems by effectively capturing high-order structural information in user-item interactions. CTNCF represents a significant advancement over the traditional Neural Collaborative Filtering (NCF) model by seamlessly integrating Convolutional Neural Networks (CNNs) and Transformer layers. This sophisticated integration enables the model to adeptly capture and understand complex interaction patterns inherent in recommendation systems. Specifically, CNNs are employed to extract local features from user and item embeddings, allowing the model to capture intricate spatial dependencies within the data. Furthermore, the utilization of Transformer layers enables the model to capture long-range dependencies and interactions among user and item features, thereby enhancing its ability to understand the underlying relationships in the data. To validate the effectiveness of our proposed CTNCF framework, we conduct extensive experiments on two real-world datasets. The results demonstrate that CTNCF significantly outperforms state-of-the-art approaches, highlighting its efficacy in improving recommendation system performance.