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About this paper
Arpit Agarwal, William Brown
A theoretical framework and algorithmic solution are introduced for recommender systems where user preferences adapt over time, demonstrating that a sublinear regret of O(T^(3/4)) can be achieved against a robust, diverse benchmark while guaranteeing content diversity. The work from Columbia University provides a principled approach for balancing reward maximization with the prevention of "rabbit hole" effects.
Key insights
- Achieving sublinear regret against naive benchmarks is impossible in adaptive preference settings, necessitating a more nuanced benchmark like "Everywhere Instantaneously-Realizable Distributions (EIRD(M))".
- For "λ-dispersed" preference models, the EIRD(M) benchmark naturally includes high-entropy distributions, suggesting that promoting diversity can also lead to more tractable optimization.
- "Local learnability" of preference models, where accurate estimation is possible from observations in a small region of the memory space, is a necessary condition for efficient learning.
Summary by alphaXiv.
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