AI RESEARCH
A Logical-Rule Autoencoder for Interpretable Recommendations
arXiv CS.LG
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ArXi:2604.04270v1 Announce Type: cross Most deep learning recommendation models operate as black boxes, relying on latent representations that obscure their decision process. This lack of intrinsic interpretability raises concerns in applications that require transparency and accountability. In this work, we propose a Logical-rule Interpretable Autoencoder (LIA) for collaborative filtering that is interpretable by design. LIA