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The drastic increase of websites is one of the causes behind the recent information overload on the internet. A recommender system (RS) has been developed for helping users filter information. However, the cold-start and sparsity problems lead to low performance of the RS. In this paper, we propose methods including the visual-clustering recommendation (VCR) method, the hybrid between the VCR and user-based methods, and the hybrid between the VCR and item-based methods. The user-item clustering is based on the genetic algorithm (GA). The recommendation performance of the proposed methods was compared with that of traditional methods. The results showed that the GA-based visual clustering could properly cluster user-item binary images. They also demonstrated that the proposed recommendation methods were more efficient than the traditional methods. The proposed VCR2 method yielded an F1 score roughly three times higher than the traditional approaches.

Top-N Recommender Systems Using Genetic Algorithm-Based Visual-Clustering Methods