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Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
Next, we design two-phase bandit algorithms that first use subsampling and low-rank matrix estimation to obtain a substantially smaller targeted set of products and then apply a UCB procedure on the target products to find the best one.
Learning to Recommend Using Non-Uniform Data
Utilizing this observation, we introduce a new optimization problem to select a weight matrix that minimizes the upper bound on the prediction error.
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