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  1. Jun 20, 2024Joint Embedding-Classifier Learning for Interpretable Collaborative Filtering. 2024. hal-04625183. Community guidelines with respect to contributions, issue reporting, and support Pull requests and issue flagging are welcome, and can be made through the GitHub interface.
  2. 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
  3. researchgate.net

    98 2.1 Knowledge graph embedding learning 99 A knowledge graph is a set of triplets of the form (h,r,t) such that the head entity 100 h is linked to the tail entity t by the relation r [17].
  4. researchgate.net

    Oct 10, 2024By 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 ...
  5. scholar.google.com

    Joint Embedding-Classifier Learning for Interpretable Collaborative Filtering. C Réda, JJ Vie, O Wolkenhauer. 2024: Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data. R Bordoloi, C Réda, O Trautmann, S Bej, O Wolkenhauer.
  6. Oct 14, 2024Background: 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 ...
  7. Joint Embedding-Classifier Learning for Interpretable Collaborative Filtering. This article has 3 authors: Clémence Réda; Jill-Jênn Vie; Olaf Wolkenhauer; This article has no evaluations Latest version Oct 14, 2024. Stay updated. Get involved. Subscribe to our newsletter.
  8. Apr 27, 2023Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN. However, the explainability of these models has not been fully explored. Adding explainability to ...
  9. researchgate.net

    Oct 19, 2024[Show full abstract] structured collaborative-filtering classifier and an embedding learning task, JELI predicts new user-item associations based on jointly learned item and user embeddings while ...
  10. sciencedirect.com

    Another direction of collaborative filtering is the estimation of the ... Authors use the deep neural networks as the classifier and apply a variety of FM models to extract features from the multi-field data. ... Y. Zhang, Q. Ai, X. Chen, W.B. Croft, Joint representation learning for top-n recommendation with heterogeneous information sources ...

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