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Recommender systems have become an essential tool for providers and users ofonline services and goods, especially with the increased use of the Internet toaccess information and purchase products and services. This work proposes anovel recommendation method based on complex networks generated by asimilarity-popularity model to predict ones. We first construct a model of anetwork having users and items as nodes from observed ratings and then use itto predict unseen ratings. The prospect of producing accurate ratingpredictions using a similarity-popularity model with hidden metric spaces anddot-product similarity is explored. The proposed approach is implemented andexperimentally compared against baseline and state-of-the-art recommendationmethods on 21 datasets from various domains. The experimental resultsdemonstrate that the proposed method produces accurate predictions andoutperforms existing methods. We also show that the proposed approach producessuperior results in low dimensions, proving its effectiveness for datavisualization and exploration.

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