Ranking Algorithms
Ranking algorithms are a set of techniques used to sort and order data based on specific criteria. In data science and artificial intelligence, ranking algorithms are used to determine the relevance of search results, recommendations, and predictions. These algorithms use various factors such as popularity, user behavior, and content quality to rank items in a list. Ranking algorithms are widely used in e-commerce, social media, and search engines to provide personalized and relevant results to users. Some popular ranking algorithms include PageRank, TF-IDF, and collaborative filtering. PageRank is used by Google to rank web pages based on their importance and relevance. TF-IDF is used to determine the importance of a word in a document based on its frequency and rarity. Collaborative filtering is used in recommendation systems to suggest items based on the preferences of similar users.
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- K-means Clustering: K-means clustering is a type of unsupervised learning algorithm used in data science and artificial intelligence to group similar data points together. The algorithm works by partitioning a dataset into k clusters, where k is a predefined n ... Read More >>
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