Learn the secret of “You may also like…” | Algolia | Algolia

The article discusses how cosine similarity, a measure of similarity between two number sequences, is used in recommendation engines and search algorithms. It explains that this metric helps determine the closeness in meaning between words or phrases, enabling search and recommender systems to identify related content or products. Cosine similarity is considered the best method for evaluating data points due to its ability to handle variable-length data and accurately measure similarity even when data objects are far apart in terms of Euclidean distance. The article also highlights how machine learning models, such as collaborative filtering algorithms and k-nearest neighbors algorithm (KNN), can be used with cosine similarity to provide high-quality recommendations based on user behavior and item attributes.