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Pré-Publication, Document De Travail Année : 2024

Joint Embedding-Classifier Learning for Interpretable Collaborative Filtering

Résumé

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. 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. We show that the joint training approach yields a gain in the predictive power of the downstream classifier, that JELI can recover feature-association dependencies, and induces a restriction in the number of parameters compared to baselines in synthetic and drug-repurposing data sets.
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Dates et versions

hal-04625183 , version 1 (26-06-2024)
hal-04625183 , version 2 (27-06-2024)

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  • HAL Id : hal-04625183 , version 1

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Clémence Réda, Jill-Jênn Vie, Olaf Wolkenhauer. Joint Embedding-Classifier Learning for Interpretable Collaborative Filtering. 2024. ⟨hal-04625183v1⟩
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