# GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation

arXiv: https://arxiv.org/abs/2111.11293 · HTML: https://mlsanity.com/paper/2111.11293/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 83 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Towards Bayesian Deep Learning: A Framework and Some Existing Methods](https://mlsanity.com/paper/1608.06884/) (2016)
- [Facial Expression Analysis under Partial Occlusion: A Survey](https://mlsanity.com/paper/1802.08784/) (2018)
- [AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders](https://mlsanity.com/paper/1704.00551/) (2017)
- [Robust Face Recognition via Multimodal Deep Face Representation](https://mlsanity.com/paper/1509.00244/) (2015)
- [Hybrid Recommender System based on Autoencoders](https://mlsanity.com/paper/1606.07659/) (2016)
- [Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes](https://mlsanity.com/paper/1408.2039/) (2014)
- [A multi-level collaborative filtering method that improves recommendations](https://mlsanity.com/paper/1804.08891/) (2018)

## Built on by (most cited first, 7 of 7)

- [Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2205.13092/) (2022)
- [Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction](https://mlsanity.com/paper/2308.02568/) (2023)
- [Hybrid Recommendation System using Graph Neural Network and BERT Embeddings](https://mlsanity.com/paper/2310.04878/) (2023)
- [Learning Semantic-Aware Representation in Visual-Language Models for Multi-Label Recognition with Partial Labels](https://mlsanity.com/paper/2412.10843/) (2024)
- [GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation](https://mlsanity.com/paper/2310.17732/) (2023)
- [SCRec: A Scalable Computational Storage System with Statistical Sharding and Tensor-train Decomposition for Recommendation Models](https://mlsanity.com/paper/2504.00520/) (2025)
- [Learning Semantic-Aware Threshold for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2507.23263/) (2025)

## Abstract (arXiv)

Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. In this paper, we propose a recommender system method using a graph-based model associated with the similarity of users' ratings, in combination with users' demographic and location information. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) has gained a significant improvement, which dominates other methods' performance in the cold-start problem. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.