JELI: an interpretable classifier

The second chapter of the RECeSS project aims at providing interpretable classification (into positive and negative associations) of drug-disease pairs. We are proud to announce that the related paper [1] is currently in press at BMC Bioinformatics!

Abstract: Background: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion. Results: We introduce the novel Joint Embedding Learning-classifier for improved Interpretability (JELI). By combining the training of a structured collaborative-filtering classifier and an embedding learning task, JELI predicts new user-item associations based on jointly learned item and user embeddings while providing feature-wise importance scores. Therefore, JELI flexibly allows the introduction of priors on the connections between users, items, and features. In particular, JELI simultaneously (a) learns feature, item, and user embeddings; (b) predicts new item-user associations; (c) provides importance scores for each feature. Moreover, JELI instantiates a generic approach to training recommender systems by encoding generic graph-regularization constraints. Conclusions: First, we show that the joint training approach yields a gain in the predictive power of the downstream classifier. Second, JELI can recover feature- association dependencies. Finally, JELI induces a restriction in the number of parameters compared to baselines in synthetic and drug-repurposing data sets.

We published the related methodological package jeli in open-source. You can also download it in PyPI. Should you encounter any issue with using those packages, please use the corresponding tab on GitHub, or simply tell us using our contact form!

We will soon publish a short blog post that highlights the main results of the paper. Stay tuned!

References

[1] Réda et al., (2024). Joint Embedding-Classifier Learning for Interpretable Collaborative Filtering (in press at BMC Bioinformatics), https://hal.science/hal-04625183/