Applying memetic algorithm-based clustering to recommender system with high sparsity problem

• Main Authors: Marung,U., Theera-Umpon,N., Auephanwiriyakul,S.
• Format: Article
• Published: Springer Science + Business Media 2015
• Subjects: Metals and Alloys; Engineering (all)
• Online Access: http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84920122519&origin=inward; http://cmuir.cmu.ac.th/handle/6653943832/39089
• Institution: Chiang Mai University

© 2014, Central South University Press and Springer-Verlag Berlin Heidelberg. A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.