Recommendation Systems Study & Discussions: Understanding Iterative Bias

Session #6
Sami Khenissi will give a talk on Understanding Iterative Bias in Recommender Systems. https://www.linkedin.com/in/samikhenissi/
https://www.samikhenissi.com/

This talk will include:--

• Understanding the different biases in Recommender Systems (Statistical bias, selection bias, diversity ...)
• Describing the effect of the Feedback loop effect on the User Discovery
• Explaining how iterative bias affects the training and evaluation process for recommender systems
• Describing methods to counteract exposure bias

● Study sessions are organized by Aki Saarinen. http://linkedin.com/in/akisaarinen

📌 Each study session will have:

● One or two presentations on topics to spark a discussion. If you’d like to present, please contact the organizers on MLT Slack (#recommendation_systems), or join the meetup and voice your interest. It can be for example learnings from a recommender related project you’re working on, an interesting paper you read, or any other relevant topic you’d like to share with our community.

● A discussion where we hope participants proactively bring in their questions, experiences, and thoughts, so we can talk together in an interactive format.

📌 Join Zoom Meeting
https://us02web.zoom.us/j/84338400633?pwd=aHRnM0QwLys3TUdyLytTVW1QL09Mdz09

📌Resources

● Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System: https://arxiv.org/abs/2008.13526
The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their...

● Degenerate Feedback Loops in Recommender Systems: https://arxiv.org/abs/1902.10730
Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affect the feedback...

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