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GACFF
View on CRAN: Click
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Download and install GACFF package within the R console
Install from CRAN:
install.packages("GACFF")
Install from Github:
library("remotes")
install_github("cran/GACFF")
Install by package version:
library("remotes")
install_version("GACFF", "1.0")
Attach the package and use:
library("GACFF")
Maintained by
Farimah Houshmand Nanehkaran
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[Scholar Profile | Author Map]
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First Published: 2019-12-20
Latest Update: 2019-12-20
Description:
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.
How to cite:
Farimah Houshmand Nanehkaran (2019). GACFF: Genetic Similarity in User-Based Collaborative Filtering. R package version 1.0, https://cran.r-project.org/web/packages/GACFF. Accessed 21 Nov. 2024.
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Complete documentation for GACFF
Functions, R codes and Examples using
the GACFF R package
Some associated functions: GACFF-package . Genetic . ItemSelect . NewKNN . Pearson . Prediction . Results . Similarity_Pearson . meanR.Results . plotResults .
Some associated R codes: Genetic.R . ItemSelect.R . NewKNN.R . Pearson.R . Prediction.R . Results.R . Similarity_Pearson.R . meanR.Results.R . plotResults.R . Full GACFF package functions and examples
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