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 methods 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 systems performance.
group recommendation system collaborative filtering technique sparsity problem user-based approach item-based approach