Weighted Regularized Matrix Factorization Algorithm
Jump to navigation
Jump to search
A Weighted Regularized Matrix Factorization Algorithm is a regularized matrix factorization algorithm that ...
- AKA: WR-MF.
- See: Unweighted Regularized Matrix Factorization.
References
2008
- (Hu et al., 2008) ⇒ Yifan Hu, Yehuda Koren, and Chris Volinsky. (2008). “Collaborative Filtering for Implicit Feedback Datasets.” In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. ISBN:978-0-7695-3502-9 doi:10.1109/ICDM.2008.22
- ABSTRACT: A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets.
We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders.