Create a Personalized Movie Recommendation Engine using Content-based Filtering in Python
Content-based recommender systems are a popular type of machine learning algorithm that recommends relevant articles based on what a user … Read more
Cosine Similarity
Here you’ll find all articles related to Cosine similarity.
Cosine similarity is a measure of similarity between two vectors. It is commonly used in natural language processing and information retrieval to compare documents or other pieces of text. The cosine similarity between two vectors is calculated as the dot product of the vectors divided by the product of their magnitudes. This value ranges from -1 to 1. Values close to 1 indicate a high similarity. Values close to -1 indicate high dissimilarity. This is a useful similarity measure because it is invariant to the magnitude of the vectors, which means that it only considers the direction of the vectors and not their length. This makes it a useful tool for comparing texts of different lengths or contexts.