Explainable Matrix Factorization for Collaborative Filtering
By Behnoush Abdollahi, Olfa Nasraoui. 2016
96 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xu Chen
- Yongfeng Zhang
- Behnoush Abdollahi
- Olfa Nasraoui
- Min Zhang
- Filip Radlinski
- Krisztian Balog
- Shushan Arakelyan
In Information Systems
- Xu Chen
- Yongfeng Zhang
- Behnoush Abdollahi
- Olfa Nasraoui
- Min Zhang
- Filip Radlinski
- Krisztian Balog
- Shushan Arakelyan
Citations in the corpus
- Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “For the first approach, Abdollahi and Nasraoui (2017) adopted mean explainability precision (MEP) and mean explainability recall (MER).”
- In2Rec: Influence-based Interpretable Recommendation (2019, methodology): “As shown in [1, 2], such differences are considered to construct the explainability matrix.”
- Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (2018, background): “Ab-dollahi and Nasraoui [1] create an Explainable Matrix Factorisation (EMF) model by adding a term to the matrix factorisation objective function which captures an ‘explainability’ score of an item for a user based on the prevalence of the item in a user’s neighbourhood.”
- Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “Others consider neighborhood-style explanations for matrix factorization (“users who are the most similar to you also liked the recommended item”) and incorporate an “explainability regularizer” into the objective function [1].”
- Using Explainability for Constrained Matrix Factorization (2017, methodology): “This article’s contribution is a probabilistic formulation that extends our previous Explainable-Matrix Factorization (EMF) model [1], for providing explainable recommendations that can leverage the accurate predictions of MF and the transparency of neighborhoodbased CF algorithms.”
- Explainable Restricted Boltzmann Machines for Collaborative Filtering (2016, methodology): “Also, (Abdollahi & Nasraoui, 2016) proposed explainable MF (EMF) method for explainable CF.”
- Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (2018, methodology): “Specifically, we first randomly select 100 test points {(uti , iti ) |i ∈ [1, 100]} from the test set.”