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Related Questions
- How can I leverage side information such as user demographics, item attributes, and user-item interaction data to improve novelty and relevance in cold start scenarios?
- What are some techniques for incorporating knowledge graphs into recommender systems to mitigate cold start problems?
- Can you discuss the use of meta-learning and transfer learning approaches to adapt to new users and items in cold start scenarios?
- How can I utilize collaborative filtering techniques such as matrix factorization and neural collaborative filtering to handle cold start problems?
- What are some strategies for leveraging user feedback and ratings to improve novelty and relevance in cold start scenarios?
- Can you explain the concept of 'item embedding' and how it can be used to handle cold start problems in recommender systems?
- How can I incorporate external knowledge sources such as text data and images to improve novelty and relevance in cold start scenarios?
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