Personalization, Algorithms, Recommendation System.
i) These predictions can be wrong, and this falsifiability risks eroding user trust. ii) If perceived as a top-n list, users might limit their exploration to the top few items, missing out on potentially other great choices and abiding by the recommendation algorithm’s biases. Shaping User