Recommender Systems with Machine Learning - Guidelines for ML

The video tutorial covers machine learning-based recommender systems, focusing on different strategies and approaches. It highlights the importance of understanding the business scenario, target audience, and product range when developing models. User-driven strategies emphasize the customer journey and preferences, while page context-driven strategies focus on product popularity and similarities. The tutorial also discusses ready-made solutions like IBM Watson for real-time personalization, aiming to enhance recommender systems.

What are the first guidelines to consider when developing a machine learning based recommended system?

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How does the business scenario influence the development of a recommended system?

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In what ways does the targeted audience affect the performance of a recommendation system?

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Why is it important to consider the product range when developing a recommendation system?

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What strategies should be employed for user-driven recommendations?

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How do page context driven strategies differ from user-driven strategies in recommendation systems?

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What are some ready-made solutions for developing better recommender systems?

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