A Recommendation Approach based on Similarity-Popularity Models of Complex Networks
This work addresses the need for accurate and efficient recommendation systems for online service providers and users, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of predicting user ratings in recommender systems by proposing a novel method based on similarity-popularity models of complex networks, and it demonstrates that this approach outperforms existing methods on 21 datasets across various domains, showing superior results in low dimensions for data visualization.
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.