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

作者：Marung Ukrit; Theera Umpon Nipon^*; Auephanwiriyakul Sansanee

来源：Journal of Central South University, 2014, 21(9): 3541-3550.

DOI：10.1007/s11771-014-2334-4

• memetic algorithm
• recommender system
• sparsity problem
• cold-start problem
• clustering method

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.

• 出版日期2014-9