Learningtorankalgoritmer
Learning to Rank algorithms, often abbreviated as LTR, are a class of supervised machine learning techniques used to solve ranking problems. Unlike traditional classification or regression, LTR aims to optimize the order of a list of items rather than predicting a specific value for each item independently. This is crucial in applications where the relative importance of results matters, such as search engine result pages, recommendation systems, and document retrieval.
LTR algorithms typically learn a scoring function that assigns a score to each item in a query-dependent
There are three main categories of LTR algorithms: point-wise, pairwise, and list-wise. Point-wise approaches treat each