In a content-based filtering algorithm, what is the next step after computing the cosine similarity matrix?Splitting the dataset into train and test sets. Evaluating system performance using RMSE.Predicting user ratings.Generating a top-N list of recommendations.

When calculating user similarity using cosine similarity, which matrix transformation is necessary before applying the similarity metric?Fill NaN values with zeros.Standardise the ratings. Transpose the utility matrix.Normalise the ratings.

Cosine similarity is a metric used to measure the similarity between two non-zero vectors in a multi-dimensional space. It's widely used in various fields, including natural language processing, information retrieval, recommendation systems, and more. Cosine similarity is particularly popular in text analysis and document retrieval tasks.

Which of the following techniques is commonly used in Recommendation Systems to suggest items based on user similarities?Review LaterCollaborative FilteringMatrix FactorizationContent-Based FilteringHybrid Filtering

Which of the following are not used for filtering in a Recommendation Engine?*Sine SimilarityCosine SimilarityJaccard SimilarityEuclidean DistanceHamming DistanceTan Distance

Which code snippet would you use to calculate the cosine similarity between all pairs of books in a dataset, given a TF-IDF matrix tfidf_matrix? similarity_matrix = np.corrcoef(tfidf_matrix)similarity_matrix = tfidf_matrix * tfidf_matrix.Tsimilarity_matrix = np.dot(tfidf_matrix, tfidf_matrix.T)similarity_matrix = cosine_similarity(tfidf_matrix,tfidf_matrix)