View source for Re-ranking

{{see also|Machine learning terms}}
==Introduction==
Re-ranking, also known as rank refinement or re-scoring, is an essential technique in [[machine learning]] that aims to improve the quality of ranked results generated by a primary ranking model. It involves using a secondary model to adjust the initial ranking produced by the primary model, based on various features and criteria. Re-ranking is widely applied in diverse fields, such as [[information retrieval]], [[natural language processing]], and [[recommender systems]].

==Re-ranking Process==
The re-ranking process consists of two primary steps:

===Primary Ranking===
The primary ranking model generates an initial ranking of items or results. This model can be based on different algorithms, such as [[support vector machines]], [[decision trees]], or [[neural networks]]. The primary ranking typically considers a limited number of features to produce a fast and efficient ranking.

===Secondary Ranking===
After obtaining the initial ranking, a secondary model is employed to refine and adjust the ranking. The secondary model may take into account additional features, context, or user preferences that were not considered by the primary model. This secondary model can be based on various techniques, including [[machine learning algorithms]], [[ensemble methods]], and [[deep learning]] architectures.

==Re-ranking Applications==
Re-ranking techniques have been applied to various domains, including:

===Information Retrieval===
In [[information retrieval]], re-ranking is employed to refine search results generated by a primary ranking model. The primary model may generate an initial ranking based on keyword matching or other simple criteria. The secondary model then considers additional features, such as document relevance, user context, or query intent, to improve the quality and relevance of the search results.

===Natural Language Processing===
In [[natural language processing]], re-ranking is used to improve the quality of outputs generated by language models, such as [[machine translation]] or [[text summarization]]. The primary model produces an initial set of candidate translations or summaries, while the secondary model adjusts the ranking based on criteria such as fluency, coherence, or content coverage.

===Recommender Systems===
Re-ranking is applied to [[recommender systems]] to refine the recommendations generated by a primary model. The primary model may produce an initial list of items based on user-item interactions, while the secondary model adjusts the ranking by incorporating additional features, such as item content, user demographics, or contextual information.

==Explain Like I'm 5 (ELI5)==
Imagine you're at a toy store, and you ask the storekeeper to show you the best toys. The storekeeper quickly picks some toys based on what they think you might like. This is like the "primary ranking" in machine learning. Then, your mom comes over and looks at the toys the storekeeper picked. She knows you better and might consider other things like safety, quality, and your interests. So, she rearranges the toys to give you the best options for you. This is like the "secondary ranking" in machine learning. The whole process of your mom rearranging the toys to give you the best choices is called "re-ranking."


[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]