Recommender Systems Complete Course Beginner to Advanced - Machine Learning for Recommender Systems: Guidelines for ML

The video tutorial discusses machine learning-based recommender systems, focusing on various approaches and strategies. It highlights the importance of understanding business scenarios, target audiences, and product ranges. User-driven strategies emphasize the customer journey and behavior, while page context-driven strategies focus on product popularity and related products. The tutorial also explores ready-made solutions like Adobe Target and IBM Watson. Finally, it introduces design strategies for developing effective 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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Why is understanding the targeted audience important for a recommendation system?

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What role does product range play in the effectiveness of a recommendation system?

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What strategies should be employed for user-driven recommendations?

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How can page context driven strategies enhance the recommendation process?

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

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