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])