How to build a Recommender System
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The document outlines the process of building a recommender system, discussing various types such as collaborative filtering, content-based filtering, and hybrid methods. It highlights the challenges faced, including the cold start problem and performance issues with large datasets. The presentation also includes practical examples and adjustments for improving recommendations and invites further contact for more information.
How to build a Recommender System
- 2. Võ Duy Tuấn Technical Director @ dienmay.com PHP 5 Zend Certified Engineer Mobile App Developer Web Developer & Designer Interest: o PHP o Large System & Data Mining o Web Performance Optimization o Mobile Development
- 3. Introduction Collaborative Filtering Question & Answer AGENDA
- 5. APPLICATIONS • Personalized recommendation • Social recommendation • Item recommendation • Combination of 3 approaches above
- 9. PROCESS DIAGRAM Preprocessing Data Analysis Adjustment INPUT OUTPUT
- 10. TYPE OF RECOMMENDER SYSTEM • Collaborative filtering • Content-based filtering • Hybrid
- 12. USER & ITEM
- 13. ORDER DATA
- 18. VECTORS
- 19. VECTORS
- 27. NEIGHBORS’ ORDER
- 30. OTHER SIMILARITY MEASURES More at: http://favi.com.vn/wp-content/uploads/2012/05/pg049_Similarity_Measures_for_Text_Document_Clustering.pdf
- 31. Problem ?!
- 32. COLLABORATIVE FILTERING PROBLEM • Fail with cold start problem o New User o New Item • Performance o Large Data set o Pre-calculate
- 33. PERFORMANCE EXAMPLE • We have 1,000,000 users (customers) • We sell 10,000 items - Total of similarity calculating = 1,000,000 x 1,000,000 = 1,000,000,000,000 - Each similarity calculate need 0.006s (on my MacBook Pro 2.2GHz Core i7, 8G Ram) => We need 1,000,000,000,000 x 0.006 = 6,000,000,000(s) ≈ 70,000 days ≈ 191 years - If store each similarity in 8 bytes, we need = 8,000,000,000,000 bytes ≈ 8,000 GB (on Memory or File)
- 34. ITEM-TO-ITEM COLLABORATIVE FILTERING (AMAZON.COM ) Download Paper: http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
- 35. ADJUSTMENTS • Hybrid Recommender System • Sale forecast system • Context of User • Type of Item, Action • External (3rd-party) information.
- 36. BOOKS Programming Collective Intelligence Toby Segaran Recommender Systems Handbook Many Authors Big Data For Dummies Marcia Kaufman, Fern Halper
- 37. OPEN SOURCES
- 38. Thank you! CONTACT ME: tuanmaster2002@yahoo.com 0938 916 902 http://bloghoctap.com/