Feature factorization for top- n recommendation: From item rating to features relevance

Proceedings of the 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning - -2017

Authors

Anelli Vito Walter, Di Noia Tommaso, Di Sciascio Eugenio, Lops Pasquale

Abstract

In the last decade, collaborative filtering approaches have shown their eectiveness in computing accurate recommendations starting from the user-item matrix. Unfortunately, due to their inner nature, collaborative algorithms work very well with dense matrices but show their limits when they deal with sparse ones. In these cases, encoding user preferences only by means of past ratings may lead to unsatisfactory recommendations. In this paper we propose to exploit past user ratings to evaluate the relevance of every single feature within each profile thus moving from a user-item to a userfeature matrix. We then use matrix factorization techniques to compute recommendations. The evaluation has been performed on two datasets referring to dierent domains (music and books) and experimental results show that the proposed method outperforms the matrix factorization approach performed in the user-item space in terms of accuracy of results.

Download: Feature factorization for top- n recommendation From item rating to features relevance.pdf

DOI

https://doi.org/10.1007/s11257-018-9217-6

BibTex references

@InProceedings{ADDL17,
  author       = "Anelli, Vito Walter and Di Noia, Tommaso and Di Sciascio, Eugenio and Lops, Pasquale",
  title        = "Feature factorization for top- n recommendation: From item rating to features relevance",
  booktitle    = "Proceedings of the 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer \& Learning ",
  volume       = "1887",
  pages        = "16--21",
  year         = "2017",
  publisher    = "CEUR-WS",
  url          = "http://sisinflab.poliba.it/Publications/2017/ADDL17"

}