Most Asked Machine Learning Interview Questions

Name and define techniques used to find similarities in the recommendation system

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Pearson correlation and Cosine correlation are techniques used to find similarities in recommendation systems. In a recommendation system, there are several techniques used to find similarities between items or users. Here are some commonly used methods: Cosine Similarity: This measures the cosine of the angle between two vectors, typically item or user vectors in a … Read more

Name and define techniques used to find similarities in the recommendation system

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In a machine learning interview, when asked about techniques used to find similarities in recommendation systems, you can discuss several common approaches along with their definitions. Here are some key techniques: Collaborative Filtering: Definition: Collaborative filtering relies on the wisdom of the crowd to make recommendations. It works by recommending items based on the preferences … Read more

How do we deal with sparsity issues in recommendation systems? How do we measure its effectiveness? Explain

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Singular value decomposition can be used to generate the prediction matrix. RMSE is the measure that helps us understand how close the prediction matrix is to the original matrix. In recommendation systems, sparsity refers to the situation where the user-item interaction matrix is sparse, meaning there are many missing entries indicating that users have not … Read more

List all types of popular recommendation systems? Name and explain two personalized recommendation systems along with their ease of implementation

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Popularity based recommendation, content-based recommendation, user-based collaborative filter, and item-based recommendation are the popular types of recommendation systems. Personalised Recommendation systems are- Content-based recommendation, user-based collaborative filter, and item-based recommendation. User-based collaborative filter and item-based recommendations are more personalised. Ease to maintain: Similarity matrix can be maintained easily with Item-based recommendation. Popular recommendation systems can … Read more

How can we use a dataset without the target variable into supervised learning algorithms?

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Input the data set into a clustering algorithm, generate optimal clusters, label the cluster numbers as the new target variable. Now, the dataset has independent and target variables present. This ensures that the dataset is ready to be used in supervised learning algorithms. Using a dataset without the target variable in supervised learning algorithms typically … Read more