sisinflab/LoG-2023-GNNs-RecSys
Presented as tutorial at the Second Learning on Graphs Conference (LoG 2023)
This tutorial helps data scientists and machine learning engineers understand and apply Graph Neural Networks (GNNs) to build more accurate recommendation systems. It takes raw user-item interaction data, potentially with additional item features like images or text, and demonstrates how GNNs can output improved recommendations by capturing complex user preferences. You would use this if you are a practitioner looking to enhance your recommender system's performance using advanced graph-based methods.
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Use this if you are a data scientist or machine learning engineer focused on recommendation systems and want to explore the practical application, reproducibility, and underlying factors influencing GNN performance.
Not ideal if you are a business user or product manager without a technical background in machine learning and graph theory.
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Dec 02, 2023
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