### Abstract

Original language | English |
---|---|

Title of host publication | RecSys '16 |

Subtitle of host publication | Proceedings of the 10th ACM Conference on Recommender Systems |

Place of Publication | Boston, Massachusetts, USA |

Publisher | ACM |

Pages | 439-442 |

Number of pages | 4 |

ISBN (Electronic) | 978-1-4503-4035-9 |

DOIs | |

Publication status | Published - 15 Sep 2016 |

### Fingerprint

### Cite this

*RecSys '16 : Proceedings of the 10th ACM Conference on Recommender Systems*(pp. 439-442). Boston, Massachusetts, USA: ACM. https://doi.org/10.1145/2959100.2959107

**Generating Pseudotransactions for Improving Sparse Matrix Factorization.** / Wibowo, Agung Toto.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*RecSys '16 : Proceedings of the 10th ACM Conference on Recommender Systems.*ACM, Boston, Massachusetts, USA, pp. 439-442. https://doi.org/10.1145/2959100.2959107

}

TY - GEN

T1 - Generating Pseudotransactions for Improving Sparse Matrix Factorization

AU - Wibowo, Agung Toto

PY - 2016/9/15

Y1 - 2016/9/15

N2 - Recent research on Recommender Systems, specifically Collaborative Filtering, has focussed on Matrix Factorization (MF) methods, which have been shown to provide good solutions to the cold start problem. However, typically the same settings are used for Matrix factorization regardless of the density of the matrix. In our experiments, we found that for MF, Root Mean Square Error (RMSE) for recommendations increases (i.e. performance drops) for sparse matrices. We propose a Two Stage MF approach so MF is run twice over the whole matrix; the first stage uses MF to generate a small percentage of pseudotransactions that are added to the original matrix to increase its density, and the second stage re-runs MF over this denser matrix to predict the user-item transactions in the testing set. We show using data from Movielens that such methods can improve on the performance of MF for sparse martrices.

AB - Recent research on Recommender Systems, specifically Collaborative Filtering, has focussed on Matrix Factorization (MF) methods, which have been shown to provide good solutions to the cold start problem. However, typically the same settings are used for Matrix factorization regardless of the density of the matrix. In our experiments, we found that for MF, Root Mean Square Error (RMSE) for recommendations increases (i.e. performance drops) for sparse matrices. We propose a Two Stage MF approach so MF is run twice over the whole matrix; the first stage uses MF to generate a small percentage of pseudotransactions that are added to the original matrix to increase its density, and the second stage re-runs MF over this denser matrix to predict the user-item transactions in the testing set. We show using data from Movielens that such methods can improve on the performance of MF for sparse martrices.

U2 - 10.1145/2959100.2959107

DO - 10.1145/2959100.2959107

M3 - Conference contribution

SP - 439

EP - 442

BT - RecSys '16

PB - ACM

CY - Boston, Massachusetts, USA

ER -