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Building Recommendation Engines by Suresh Kumar Gorakala

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Need for recommender systems

Given the complexity and challenges in building recommendation engines, a considerable amount of thought, skill, investment, and technology goes into building recommender systems. Are they worth such an investment? Let us look at some facts:

  • Two-thirds of movies watched by Netflix customers are recommended movies
  • 38% of click-through rates on Google News are recommended links
  • 35% of sales at Amazon arise from recommended products
  • ChoiceStream claims that 28% of people would like to buy more music, if they find what they like

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