👉 Ranking computation is a process used in information retrieval and machine learning to determine the relevance or importance of items, such as web pages, documents, or items in a recommendation system, based on a ranked list. It involves assigning scores or ranks to these items, where higher ranks indicate greater relevance or preference. This is typically achieved through algorithms that analyze various features and attributes of the items, such as content, user behavior, or metadata, to predict how well each item matches a given query or user preference. Ranking computation often employs techniques like learning-to-rank (LTR) models, which use machine learning to optimize these scores, or traditional methods like TF-IDF and BM25 for simpler ranking tasks. The goal is to produce a ranked list that best satisfies the user's information needs, whether it's finding the most relevant search results or suggesting the most pertinent items in a recommendation engine.
