SHAP & Game Theory For Recommendation Systems

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Shap for recommendation systems: How to use existing Machine Learning models as a recommendation system. We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator fulfills the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.

Speaker: Avi Ben Yossef

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About Avi Ben Yossef

First Digital Bank

Director of data science and AI, Big Data & Machine Learning Expert, with over 10 years of experience in building various systems, both from the field of machine learning, recommendation, Big Data, and optimization systems.

Lead of data science team responsible to develop algorithms for solving diverse business challenges, by designing, implementing and developing a unique research operation, ML methods & infrastructure.