Enhancing memory-based collaborative filtering for group recommender systems.

Enhancing memory-based collaborative filtering techniques for group recommender systems by resolving the data sparsity problem.Comparing the proposed method's accuracy with basic memory-based techniques and latent factor model.Makeing accurate predictions for unknown ratings in sparse matrices based on the proposed method.More users are satisfied of the group recommender system's performance. Memory-based collaborating filtering techniques are widely used in recommender systems. They are based on full initial ratings in a user-item matrix. However, most of the time in group recommender systems, this matrix is sparse and users' preferences are unknown. This deficiency may make memory-based collaborative filtering unsuitable for group recommender systems. This paper, improves memory-based techniques for group recommendation systems by resolving the data sparsity problem. The core of the proposed method is based on a support vector machine learning model that computes similarities between items. This method employs calculated similarities and enhances basic memory-based techniques. Experiments demonstrate that the proposed method overcomes the memory-based techniques. It also indicates that the presented work outperforms the latent factor approach, which is very efficient in sparse conditions. Finally, it is indicated that the proposed method gives a better performance than existing approaches on generating group recommendations.