Personalized Ranking Model 최적화 방식이 세 가지로 나뉘어짐 pointwise Pointwise approaches considers a single interaction at a time and train a classifier or a regressor to predict individual preferences. 예시) matrix factorization, AutoRec pairwise Pairwise approaches consider a pair of items for each user and aim to approximate the optimal ordering for that pair. 일반적으로 pairwise 방식이 ranking 업무에 더욱 적절하다. 예시) BPR - Bayesian Personalized Ranking from Implicit Feedback listwise Listwise approaches approximate the ordering of the entire list of items, for example, direct optimizing the ranking measures such as Normalized Discounted Cumulative Gain (Normalized Discounted Cumulative Gain). pointwise 나 pairwise 방식보다 복잡하며 계산 비용이 높다. B) Related C) References