A type of Algorithm that is used in recommendation systems. It uses probability to prioritize actions. Still in the process of reading the material and trying to understand how exactly it works. However, to quote the paper;

The idea behind posterior sampling algorithm is to force

optimism through probabilistic action. Specifically at each

time step, t, we will make a recommendation j(t) based on

the probability that it is the best possible recommendation,

P j(t) = j∗(t). However, this probability is inaccessible, so

instead the algorithm samples a model for the unknown feature

vectors based on the probability that they are the true feature

vectors (given the viewing history), and finds the optimal

recommendation should this be the true model. It can be

shown that this sampling technique is equivalent to sampling

a recommendation based on the probability it is optimal, and a

more detailed description of the algorithm and its motivations

can be seen in. Thus the algorithm proceeds to keep track

of the distribution of model parameters at each time step, and

updates them accordingly

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