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 context. The goal is to arrange the items such that the most relevant ones appear at the top of the ranked list. Training data for LTR usually consists of queries and associated lists of documents, along with relevance judgments indicating how well each document answers a particular query. These judgments can be binary (relevant/irrelevant) or graded (e.g., perfect, excellent, good, fair, bad).

There are three main categories of LTR algorithms: point-wise, pairwise, and list-wise. Point-wise approaches treat each document-query pair as an independent instance, predicting its relevance score. Pairwise methods focus on learning a preference relationship between pairs of documents for a given query, aiming to correctly order pairs. List-wise approaches consider the entire ranked list of documents for a query as a single instance, optimizing a ranking metric directly. LTR has become a fundamental component in many information retrieval and recommendation systems due to its ability to adapt and improve ranking quality based on observed user interactions and explicit relevance feedback.