# An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

arXiv: https://arxiv.org/abs/1908.04032 · HTML: https://mlsanity.com/paper/1908.04032/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation](https://mlsanity.com/paper/2109.13527/) (2021)
- [A Graph-Enhanced Click Model for Web Search](https://mlsanity.com/paper/2206.08621/) (2022)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations](https://mlsanity.com/paper/2306.17256/) (2023)
- [Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://mlsanity.com/paper/2201.13311/) (2022)
- [NudgeRank: Digital Algorithmic Nudging for Personalized Health](https://mlsanity.com/paper/2407.20241/) (2024)
- [Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation](https://mlsanity.com/paper/2308.15244/) (2023)
- [Co-Pilot for Health: Personalized Algorithmic AI Nudging to Improve Health Outcomes](https://mlsanity.com/paper/2401.10816/) (2024)

## Abstract (arXiv)

This paper studies graph-based recommendation, where an interaction graph is constructed from historical records and is lever-aged to alleviate data sparsity and cold start problems. We reveal an early summarization problem in existing graph-based models, and propose Neighborhood Interaction (NI) model to capture each neighbor pair (between user-side and item-side) distinctively. NI model is more expressive and can capture more complicated structural patterns behind user-item interactions. To further enrich node connectivity and utilize high-order structural information, we incorporate extra knowledge graphs (KGs) and adopt graph neural networks (GNNs) in NI, called Knowledge-enhanced NeighborhoodInteraction (KNI). Compared with the state-of-the-art recommendation methods,e.g., feature-based, meta path-based, and KG-based models, our KNI achieves superior performance in click-through rate prediction (1.1%-8.4% absolute AUC improvements) and out-performs by a wide margin in top-N recommendation on 4 real-world datasets.