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How to build a Recommender System

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Võ Duy Tuấn
Võ Duy Tuấn

This presentation show the method to build a Recommender System with Collaborative FIltering method.

Technology
1 of 38
Recommender System How to build a
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
Introduction Collaborative Filtering Question & Answer AGENDA
1. Introduction
APPLICATIONS • Personalized recommendation • Social recommendation • Item recommendation • Combination of 3 approaches above
AMAZON.COM | BOOKS
PLAY.GOOGLE.COM | APPS
SKILLSHARE.COM | CLASSES
PROCESS DIAGRAM Preprocessing Data Analysis Adjustment INPUT OUTPUT
TYPE OF RECOMMENDER SYSTEM • Collaborative filtering • Content-based filtering • Hybrid
2. Collaborative Filtering
USER & ITEM
ORDER DATA
ORDER DATA (cont.)
ORDER DATA (cont.)
VECTOR & DIMENSION
VECTOR & DIMENSION
VECTORS
VECTORS
SIMILARITY CALCULATION
USER SIMILARITY MATRIX
SIMILARITY CALCULATION
SIMILARITY CALCULATION
SIMILARITY CALCULATION EXAMPLE
K-NEAREST-NEIGHBOR
K-NEAREST-NEIGHBOR
NEIGHBORS’ ORDER
REMOVE BOUGHT ITEMS
CALCULATING FINAL SCORE
OTHER SIMILARITY MEASURES More at: http://favi.com.vn/wp-content/uploads/2012/05/pg049_Similarity_Measures_for_Text_Document_Clustering.pdf
Problem ?!
COLLABORATIVE FILTERING PROBLEM • Fail with cold start problem o New User o New Item • Performance o Large Data set o Pre-calculate
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)
ITEM-TO-ITEM COLLABORATIVE FILTERING (AMAZON.COM ) Download Paper: http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
ADJUSTMENTS • Hybrid Recommender System • Sale forecast system • Context of User • Type of Item, Action • External (3rd-party) information.
BOOKS Programming Collective Intelligence Toby Segaran Recommender Systems Handbook Many Authors Big Data For Dummies Marcia Kaufman, Fern Halper
OPEN SOURCES
Thank you! CONTACT ME: tuanmaster2002@yahoo.com 0938 916 902 http://bloghoctap.com/

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