Mis on pointwise, pairwise ja listwise lähenemised learning to rank'is?

Learning to rank (LTR) involves three main approaches to training ranking models: pointwise, pairwise, and listwise. They differ in how they consider data and how they formulate the loss function.

• Pointwise: considers each document separately as an individual example with a relevance label. The model learns to predict the relevance of each document independently. For example, the task reduces to regression or classification.

• Pairwise: considers pairs of documents and learns to predict which of the two is more relevant. The loss function is based on pairwise comparisons, allowing for better consideration of the relative order.

• Listwise: considers the entire list of documents for a single query at once and optimizes the loss function related to the quality of the entire ranking (e.g., NDCG). This is a more complex but also more accurate approach.

Each approach has its advantages and is applied depending on the task and available data.