Optimising latent features using artificial immune system in collaborative filtering for recommender systems

The performance of the AISMF is compared to that of the user-based and item-based neighbourhood clustering methods, SGD, Slope-one and Tendency-based methods. The results show that the AISMF converges faster to local minima for small to medium sized datasets and the AISMF ensemble performs better and faster, on average, on large datasets. The results also show that the AISMF ensemble is comparable to that of the SGD, user-based, item-based, Slope-one and Tendency-based methods in CF and can be used as an alternative learning and recommendation method in CF.