Bayesian vs Frequentist Reasoning

Bayesian statistics concerns itself with trying to represent your beliefs in a DeFinetti consistent manner, simply put - you want a logically rigorous way of describing your initial (prior) belief and the update in your belief as you observe new data (thus creating posterior belief).

Frequentist statistics concerns itself with methods that have ‘long run’ guarantees.

E.g. If a person shoots bullseye of the target 9 times, the frequentist approach predicts that 10th shot will also be bullseye. Whereas, as a human we are prone to making some error. The Bayesian approach will take this prior behavior into account. It will not predict 1 as the probability of hitting bullseye in 10th shot. It will take into account prior shots in similar situations and predict accordingly.

In case of Web data, if a person has liked something 9 times, frequentist will predict that the person will like it 10th time as well. Where as, Bayesian approach knows that humans can get bored. Liking something 9 times does not guarantee that it will be liked 10th time as well.