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Brief facts about l2r:

Learning to rank or machine-learned ranking is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.

Content-based image retrieval - Content-based image retrieval, also known as query by image content and content-based visual information retrieval, is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases.

Multimedia information retrieval - Multimedia information retrieval is a research discipline of computer science that aims at extracting semantic information from multimedia data sources.

Image retrieval - An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images.

Triplet loss - Triplet loss is a loss function for artificial neural networks where a baseline input is compared to a positive input and a negative input. The distance from the baseline input to the positive input is minimized, and the distance from the baseline input to the negative input is maximized.

Ranking functions

Information retrieval techniques

Machine learning