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Found 6 papers, 2 papers with code
Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles
We also observe a sudden decrease of misinformation filter bubble effect when misinformation debunking videos are watched after misinformation promoting videos, suggesting a strong contextuality of recommendations.
Pessimistic Off-Policy Optimization for Learning to Rank
Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy.
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
We present a study in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content (for various topics).
Exploring Customer Price Preference and Product Profit Role in Recommender Systems
We propose an adjustment of a predicted ranking for score-based recommender systems and explore the effect of the profit and customers' price preferences on two industry datasets from the fashion domain.
The Cold-start Problem: Minimal Users' Activity Estimation
Cold-start problem, which arises upon the new users arrival, is one of the fundamental problems in today's recommender approaches.
Session-based k-NNs with Semantic Suggestions for Next-item Prediction
One of the most critical problems in e-commerce domain is the information overload problem.
