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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Published on: March 1, 2022

Joint embedding-classifier learning for interpretable collaborative filtering.

Clémence Réda¹, Jill-Jênn Vie², Olaf Wolkenhauer^3,4,5

¹Institute of Computer Science, University of Rostock, 18051, Rostock, Germany. clemence.reda@uni-rostock.de.

BMC Bioinformatics

|January 23, 2025

Summary

We developed JELI, a new method for interpretable recommender systems. JELI improves prediction accuracy and identifies feature importance, crucial for healthcare applications.

Keywords:

Collaborative filtering Drug repurposing Gene expression Interpretability

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Area of Science:

Machine Learning
Recommender Systems
Artificial Intelligence

Background:

Interpretability is a critical challenge in recommender systems, particularly within healthcare.
Existing interpretable classifiers struggle to unambiguously quantify feature importance for item-user associations.

Purpose of the Study:

To introduce JELI (Joint Embedding Learning-classifier for improved Interpretability), a novel method enhancing recommender system interpretability.
To provide feature-wise importance scores for predicted user-item associations.

Main Methods:

JELI combines structured collaborative-filtering classification with embedding learning.
It jointly learns feature, item, and user embeddings.
The approach incorporates generic graph-regularization constraints for flexible prior introductions.

Main Results:

JELI demonstrates improved predictive power in downstream classification tasks.
The method successfully recovers feature-association dependencies.
JELI reduces the number of parameters compared to baseline methods on synthetic and drug-repurposing datasets.

Conclusions:

Joint training enhances the predictive capabilities of recommender system classifiers.
JELI offers a robust solution for interpretable recommendations, especially in sensitive domains like healthcare.
The method's efficiency in parameter usage makes it a valuable advancement.