# A Recommendation Approach based on Similarity-Popularity Models of Complex Networks

arXiv: https://arxiv.org/abs/2210.07816 · HTML: https://mlsanity.com/paper/2210.07816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Generating Personalized Recipes from Historical User Preferences](https://mlsanity.com/paper/1909.00105/) (2019)
- [Network Geometry](https://mlsanity.com/paper/2001.03241/) (2020)
- [The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review](https://mlsanity.com/paper/1511.05263/) (2015)
- [Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems](https://mlsanity.com/paper/1610.08095/) (2016)
- [A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities](https://mlsanity.com/paper/1707.07325/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Sequence-Aware Recommendation Method Based on Complex Networks](https://mlsanity.com/paper/2210.07814/) (2022)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)

## Abstract (arXiv)

Recommender systems have become an essential tool for providers and users of online services and goods, especially with the increased use of the Internet to access information and purchase products and services. This work proposes a novel recommendation method based on complex networks generated by a similarity-popularity model to predict ones. We first construct a model of a network having users and items as nodes from observed ratings and then use it to predict unseen ratings. The prospect of producing accurate rating predictions using a similarity-popularity model with hidden metric spaces and dot-product similarity is explored. The proposed approach is implemented and experimentally compared against baseline and state-of-the-art recommendation methods on 21 datasets from various domains. The experimental results demonstrate that the proposed method produces accurate predictions and outperforms existing methods. We also show that the proposed approach produces superior results in low dimensions, proving its effectiveness for data visualization and exploration.