AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This reproducibility paper explores the counterfactual explanation technique known as Learning to eXplain Recommendations (LXR), and considers alternate baselines for LXR, including item popularity and latent factor similarity, and finds that for some tasks these alternatives work surprisingly well.
Transparency in recommender systems has been widely studied from the perspective of those receiving recommendations, yet the needs of item providers, the creators whose content is distributed through these platforms, remain largely unexplored. Providers often lack insight into how their items do or do not receive expos...
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