GitHub / NaynaJahan / Best-Ad-selection-to-optimize-CTR-using-Reinforcement-Learning-Thompson-Sampling-

Best Ad selection among many advertisements shown to different users/website viewer to optimize Click-through rate using Thompson Sampling - a reinforcement learning approach. As the customer navigates the website, they will suddenly get a pop-up ad, suggesting to them that they subscribe to the premium plan. For each customer browsing the website, only one of the nine strategies will be displayed. Then the user will choose, or not, to take action and subscribe to the premium plan. If the customer subscribes, the strategy is a success; otherwise, it is a failure. The more customers we do this with, the more feedback we collect, and the better idea we get of what the best strategy isThe data is taken through simulation and the best ad is shown using a histogram.

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Topics: machine-learning, reinforcement-learning, thompson-sampling

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