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

TL;DR

This paper introduces a novel recommendation method based on complex network models that effectively predicts user ratings, outperforming existing approaches across diverse datasets and demonstrating advantages in low-dimensional data visualization.

Contribution

The paper presents a new recommendation approach using similarity-popularity models with hidden metric spaces, enhancing prediction accuracy and data visualization capabilities.

Findings

Outperforms baseline and state-of-the-art methods on 21 datasets.

Effective in low-dimensional data visualization and exploration.

Produces accurate rating predictions across various domains.

Abstract

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.