Bandits for Recommender Systems

Bandits for Recommender Systems

Bandits for Recommender Systems

“Recommender systems work well when we have a lot of data on user-item preferences. With a lot of data, we have high certainty about what users like. Conversely, with very little data, we have low certainty. Despite the low certainty, recommenders tend to greedily promote items that received higher engagement in the past. And because they influence how much exposure an item gets, potentially relevant items that aren’t recommended continue getting no to low engagement, perpetuating the feedback loop…”

Source: eugeneyan.com/writing/bandits/