GACFF - Genetic Similarity in User-Based Collaborative Filtering
The genetic algorithm can be used directly to find the
similarity of users and more effectively to increase the
efficiency of the collaborative filtering method. By
identifying the nearest neighbors to the active user, before
the genetic algorithm, and by identifying suitable starting
points, an effective method for user-based collaborative
filtering method has been developed. This package uses an
optimization algorithm (continuous genetic algorithm) to
directly find the optimal similarities between active users
(users for whom current recommendations are made) and others.
First, by determining the nearest neighbor and their number,
the number of genes in a chromosome is determined. Each gene
represents the neighbor's similarity to the active user. By
estimating the starting points of the genetic algorithm, it
quickly converges to the optimal solutions. The positive point
is the independence of the genetic algorithm on the number of
data that for big data is an effective help in solving the
problem.
