Which technique aims to fill in missing entries in a user-item interaction matrix to generate personalized recommendations?Review LaterHybrid FilteringMatrix FactorizationCollaborative FilteringNone of the above

Which technique is suitable for providing recommendations in the absence of user-item interaction data?Review LaterCollaborative FilteringContent-Based FilteringMatrix FactorizationHybrid Filtering

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 type of collaborative filtering algorithm utilizes item-item similarities to generate recommendations?Question 23Answera. Item-based collaborative filteringb.Matrix factorizationc.Content-based filteringd.User-based collaborative filtering

Which technique considers the characteristics and features of items to make recommendations based on their similarity to previously liked items by the user?Review LaterCollaborative filteringContent-based filteringActive learningReinforcement learning

Which technique is more effective in handling the "cold start" problem in Recommendation Systems?Review LaterCollaborative FilteringMatrix FactorizationContent-Based FilteringHybrid Filtering