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Everything You Need to Know About Recommendation Systems

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Tu Pham
Tu Pham

A recommendation system attempts to predict items a user may be interested in, like movies, music or books, to help people find interesting information. For ecommerce, recommendation systems can suggest additional or more expensive items. Common approaches include collaborative filtering based on user preferences, content-based filtering using item descriptions, and hybrid methods. Effective recommendation systems face challenges like data sparsity, scalability, and "shilling" attacks. Major companies report significant sales increases from recommendation features, showing their business value.

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Tu Pham RECOMMENDATION SYSTEM
What is RECOMMENDATION SYSTEM • Systems that attempt to predict items, e.g., movies, music, books, that a user may be interested in • Systems that help people find information that will interest them, by facilitating social / conceptual connections or other means…
RS FOR ECOMMERCE • Cross sale: E.g. Frequently bought together • Up sale: E.g. Sale the another expensive version of current item • Search suggestion • Personal recommenadation via email marketing
EXPLAINATION
APPROACHES • Collaborative filtering: Recommend items based only on the users past behavior • User-based: Find similar users to me and recommend what they liked • Item-based: Find similar items to those that I have previously liked • Content-based: build a model based on a description of the item and a profile of the user’s preference, keywords are used to describe the items; beside, a user profile is built to indicate the type of item this user likes. • Hybrid: Combine any of the above
THE MATRIX
ABOUT AMAZON ALGORITHM
PROBLEM • Data sparsity • Scalability • Synonyms • Grey sheep • Shilling attacks • Diversity and the Long Tail • => This stuff is complex
The value AND SOME FACTS • The Value of RS • AMAZON: 35% sales from recommendations • GOOGLE NEWS: recommendations generate 38% more clickthrough • NETFLIX: 2/3 of the movies watched are recommended • Some facts • Up sale is more effficient than cross sale (from Amazon) • Item based collaborative filtering is more effficient than user based
THANKS YOU!

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Everything You Need to Know About Recommendation Systems

  • 2. What is RECOMMENDATION SYSTEM • Systems that attempt to predict items, e.g., movies, music, books, that a user may be interested in • Systems that help people find information that will interest them, by facilitating social / conceptual connections or other means…
  • 3. RS FOR ECOMMERCE • Cross sale: E.g. Frequently bought together • Up sale: E.g. Sale the another expensive version of current item • Search suggestion • Personal recommenadation via email marketing
  • 5. APPROACHES • Collaborative filtering: Recommend items based only on the users past behavior • User-based: Find similar users to me and recommend what they liked • Item-based: Find similar items to those that I have previously liked • Content-based: build a model based on a description of the item and a profile of the user’s preference, keywords are used to describe the items; beside, a user profile is built to indicate the type of item this user likes. • Hybrid: Combine any of the above
  • 6.
  • 9. PROBLEM • Data sparsity • Scalability • Synonyms • Grey sheep • Shilling attacks • Diversity and the Long Tail • => This stuff is complex
  • 10. The value AND SOME FACTS • The Value of RS • AMAZON: 35% sales from recommendations • GOOGLE NEWS: recommendations generate 38% more clickthrough • NETFLIX: 2/3 of the movies watched are recommended • Some facts • Up sale is more effficient than cross sale (from Amazon) • Item based collaborative filtering is more effficient than user based