# An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

arXiv: https://arxiv.org/abs/2007.00216 · HTML: https://mlsanity.com/paper/2007.00216/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 29 of 29)

- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications](https://mlsanity.com/paper/2202.07893/) (2022)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Multi-Behavior Sequential Recommendation with Temporal Graph Transformer](https://mlsanity.com/paper/2206.02687/) (2022)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://mlsanity.com/paper/2201.13311/) (2022)
- [RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation](https://mlsanity.com/paper/2407.20684/) (2024)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)
- [Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks](https://mlsanity.com/paper/2211.14752/) (2022)
- [Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems](https://mlsanity.com/paper/2106.04993/) (2021)
- [SHGNN: Structure-Aware Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2112.06244/) (2021)
- [HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks](https://mlsanity.com/paper/2208.02068/) (2022)
- [All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction](https://mlsanity.com/paper/2403.17740/) (2024)
- [Simple and Efficient Heterogeneous Temporal Graph Neural Network](https://mlsanity.com/paper/2510.18467/) (2025)
- [Refined Edge Usage of Graph Neural Networks for Edge Prediction](https://mlsanity.com/paper/2212.12970/) (2022)
- [InBox: Recommendation with Knowledge Graph using Interest Box Embedding](https://mlsanity.com/paper/2403.12649/) (2024)
- [Knowledge Graph Pruning for Recommendation](https://mlsanity.com/paper/2405.11531/) (2024)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices](https://mlsanity.com/paper/2204.07818/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation](https://mlsanity.com/paper/2309.13384/) (2023)
- [Adaptive spectral graph wavelets for collaborative filtering](https://mlsanity.com/paper/2312.03167/) (2023)

## Abstract (arXiv)

There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics. Although the existing approaches have achieved performance improvement, while practical, they still face the following problems. On one hand, most existing HIN-based methods rely on explicit path reachability to leverage path-based semantic relatedness between users and items, e.g., metapath-based similarities. These methods are hard to use and integrate since path connections are sparse or noisy, and are often of different lengths. On the other hand, other graph-based methods aim to learn effective heterogeneous network representations by compressing node together with its neighborhood information into single embedding before prediction. This weakly coupled manner in modeling overlooks the rich interactions among nodes, which introduces an early summarization issue. In this paper, we propose an end-to-end Neighborhood-based Interaction Model for Recommendation (NIRec) to address the above problems. Specifically, we first analyze the significance of learning interactions in HINs and then propose a novel formulation to capture the interactive patterns between each pair of nodes through their metapath-guided neighborhoods. Then, to explore complex interactions between metapaths and deal with the learning complexity on large-scale networks, we formulate interaction in a convolutional way and learn efficiently with fast Fourier transform. The extensive experiments on four different types of heterogeneous graphs demonstrate the performance gains of NIRec comparing with state-of-the-arts. To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.