Matrix factorization
Matrix factorization approaches consider that the adjacency matrix of the social network can be factorized in a group of two or three matrices of smaller dimension. We include the following approaches:
Implicit matrix factorization
Matrix factorization algorithm designed to deal with implicit feedback data in recommender systems.
Reference: Y. Hu, Y. Koren, C. Volinsky. Collaborative filtering for implicit feedback datasets, International Conference on Data Mining (ICDM 2008) (2008).
Parameters
lambda
: regulates the importance of the error and the norm of the latent vectors.alpha
: weights the confidence on the weight of the edges.k
: the number of latent factors for each user.weighted
: (OPTIONAL) true to use the weights of the edges, false to consider them binary.
Configuration file
iMF:
lambda:
type: double
values: [0.1,1,10,100,150]
alpha:
type: double
values: [1,10,40,100]
k:
type: int
range:
- start: 10
end: 300
step: 10
(weighted:
type: boolean
values: [true,false])
Fast implicit matrix factorization
Fast matrix factorization algorithm designed to deal with implicit feedback data in recommender systems.
Reference: I. Pilászy, D. Zibriczky and D. Tikk. Fast ALS-based Matrix Factorization for Explicit and Implicit Feedback Datasets. 4th ACM Conference on Recommender Systems (RecSys 2010),71–78 (2010).
Parameters
lambda
: regulates the importance of the error and the norm of the latent vectors.alpha
: weights the confidence on the weight of the edges.k
: the number of latent factors for each user.weighted
: (OPTIONAL) true to use the weights of the edges, false to consider them binary.
Configuration file
Fast iMF:
lambda:
type: double
values: [0.1,1,10,100,150]
alpha:
type: double
values: [1,10,40,100]
k:
type: int
range:
- start: 10
end: 300
step: 10
(weighted:
type: boolean
values: [true,false])