Table 4.

Brief comparative overview on the learn to rank approaches, adapted from [ 34 ]

	Pairwise learn to rank	Listwise learn to rank
Goal	Ranking by learning on object pairs	Ranking by learning on object lists
Loss function	pairwise loss, e.g., hinge loss, exponential loss, logistic loss	listwise loss, e.g., cross entropy loss, cosine loss
Advantages	Theoretical aspects are well studied	Considers the relationship among objects to their full extent
Disadvantages	Considers only pairwise orders; May be biased towards lists with more objects	Theoretical aspects are less well studied
Algorithms	SVMRank [19]; RankNet [21]; RankBoost [20]	ListNet [22]