✨Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems
📝 Summary:
A Sparse Autoencoder extracts interaction-aware monosemantic concepts from recommender embeddings. Its prediction-aware training aligns these with model predictions, enabling controllable personalization and interpretability.
🔹 Publication Date: Published on Nov 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18024
• PDF: https://arxiv.org/pdf/2511.18024
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#RecommenderSystems #DeepLearning #AI #Interpretability #Personalization
📝 Summary:
A Sparse Autoencoder extracts interaction-aware monosemantic concepts from recommender embeddings. Its prediction-aware training aligns these with model predictions, enabling controllable personalization and interpretability.
🔹 Publication Date: Published on Nov 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18024
• PDF: https://arxiv.org/pdf/2511.18024
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#RecommenderSystems #DeepLearning #AI #Interpretability #Personalization