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Introduction to Recommendation Systems


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1) Recommendation Systems in Practice
2) Types of Recommendation Systems
3) Building Data Pipeline for Video Recommendation System

Published in: Data & Analytics
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Introduction to Recommendation Systems

  1. 1. Introduction to Recommendation Systems for News, Education and Entertainment By Trieu Nguyen - Lead Engineer at FPT Telecom My email:
  2. 2. How computers know what we really want ? By using AI, Big Data or Data Science !?
  3. 3. People don’t know what they want until you recommend it to them.
  4. 4. 3 key ideas about data 1. Data Product 2. Data Engineering 3. Data Science 1. Recommendation Systems in Practice 2. Types of Recommendation Systems 3. Building Data Pipeline for Video Recommendation System 3 key ideas about the slide
  5. 5. After this talk, I hope everyone can understand above big picture
  6. 6. 1 - The World of Recommendation Systems
  7. 7. Google News
  8. 8. LinkedIn: "Jobs you may be interested in"
  9. 9. Your Recommendations from MOOC Coursera
  10. 10. “Recommended for you” by YouTube User-based Collaboration Filter Item-based Collaboration Filter
  11. 11. Amazon Recommendation Engine
  12. 12. That my books, mostly from Amazon :)
  13. 13.
  14. 14. WHY SHOULD WE USE RECOMMENDATION ENGINES? 1. Two-thirds of movies watched by Netflix customers are recommended movies 2. 38% of click-through rates on Google News are recommended links 3. 35% of sales at Amazon arise from recommended products Steve Jobs: “A lot of times, people don’t know what they want until you show it to them.”
  15. 15. Beneficial features of the product recommendation engine to marketers 1. Retain user loyalty 2. Builds the volume of user traffic 3. Delivers more convenient UX to your user 4. Give your business a wider marketplace
  16. 16. So what is recommendation engine ? In technical terms, a recommendation engine problem is to develop a mathematical model or objective function which can predict how much a user will like an item. If U = {users}, I = {items} then F = Objective function and measures the usefulness of item I to user U, given by: F: U x I → R Where R = {recommended items}. For each user u, we want to choose the item i that maximizes the objective function:
  18. 18. 3 important types of recommender systems 1. Collaborative Filtering 2. Content-Based Filtering 3. Hybrid Recommendation Systems
  19. 19. User-based Collaborative Filtering Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an “understanding” of the item itself
  20. 20. User-based Collaborative Filtering
  21. 21. Content Based Filtering ( Item-based collaborative filtering) Content-based filtering methods are based on a description of the item and a profile of the user’s preference. In a content-based recommendation system, keywords are used to describe the items; beside, a user profile is built to indicate the type of item this user likes.
  22. 22. Content Based Filtering ( Item-based collaborative filtering)
  23. 23. Hybrid Recommendation Systems These methods can also be used to overcome some of the common problems in recommendation systems such as cold start and the sparsity problem.
  24. 24. Hybrid Recommendation Systems
  25. 25. Example of Hybrid Recommendation Systems User-based Collaboration Filter Item-based Collaboration Filter
  26. 26. User-based VS Item-based
  27. 27. 3 - Building Data Pipeline for Video Recommendation System
  28. 28. Simple version of Video Data Pipeline
  29. 29. Simple Recommendation Engine with Apache Spark MLLib
  30. 30. My personal answer is “building 2 things” 1. Data Ecosystem 2. Choice Architecture How computers know what we really want ?
  31. 31. Data Ecosystem with { Product + Engineering + Science }
  32. 32. Choice Architecture for recommendation systems
  33. 33. Picture from
  34. 34.
  35. 35. Follow this page to get more information
  36. 36. Ref links about Apache Spark -engine-with-spark-mllib-and-play th-mllib.html ark-python-example-app-part1-du1083qbw od-for-collaborative-filtering/
  37. 37. Ref links