Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems
We present a method for extracting monosemantic neurons, defined as latent dimensions that align with coherent and interpretable concepts, from user and item embeddings in recommender systems. Our approach employs a Sparse Autoencoder (SAE) to reveal semantic structure within pretrained representations. In contrast to work on language models, monosemanticity in recommendation must preserve the interactions between separate user and item embeddings. To achieve this, we introduce a prediction aware training objective that backpropagates through a frozen recommender and aligns the learned latent structure with the model's user-item affinity predictions. The resulting neurons capture properties such as genre, popularity, and temporal trends, and support post hoc control operations including targeted filtering and content promotion without modifying the base model. Our method generalizes across different recommendation models and datasets, providing a practical tool for interpretable and controllable personalization. Code and evaluation resources are available at https://github.com/DeltaLabTLV/Monosemanticity4Rec.
6925312216eb3a9f13103a40
Dor Arviv
claimed_verified
2026-02-17T15:48:56.911Z
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Dor Arviv
dor-arviv1
user
dor-arviv1
6925312216eb3a9f13103a41
Yehonatan Elisha
admin_assigned
2025-11-25T09:34:23.580Z
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Yehonatan Elisha
Yoniel
user
Yoniel
6925312216eb3a9f13103a42
Oren Barkan
6925312216eb3a9f13103a43
Noam Koenigstein
admin_assigned
2025-11-25T09:34:43.704Z
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Noam Koenigstein
deltalab-noam
user
deltalab-noam
Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems
We present a method for extracting monosemantic neurons, defined as latent dimensions that align with coherent and interpretable concepts, from user and item embeddings in recommender systems. Our approach employs a Sparse Autoencoder (SAE) to reveal semantic structure within pretrained representations. In contrast to work on language models, monosemanticity in recommendation must preserve the interactions between separate user and item embeddings. To achieve this, we introduce a prediction aware training objective that backpropagates through a frozen recommender and aligns the learned latent structure with the model's user-item affinity predictions. The resulting neurons capture properties such as genre, popularity, and temporal trends, and support post hoc control operations including targeted filtering and content promotion without modifying the base model. Our method generalizes across different recommendation models and datasets, providing a practical tool for interpretable and controllable personalization. Code and evaluation resources are available at https://github.com/DeltaLabTLV/Monosemanticity4Rec.
A Sparse Autoencoder method extracts interpretable latent dimensions from user and item embeddings in recommender systems, aligning with model predictions and supporting controllable personalization.
Sparse Autoencoder monosemantic neurons latent dimensions pretrained representations prediction aware user-item affinity predictions targeted filtering content promotion