• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
How to build a Recommender System
 

How to build a Recommender System

on

  • 3,308 views

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

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

Statistics

Views

Total Views
3,308
Views on SlideShare
3,201
Embed Views
107

Actions

Likes
25
Downloads
154
Comments
5

4 Embeds 107

http://www.barcampsaigon.org 91
http://spiral.teamcrop.com 9
http://urtuts.com 5
https://gitter.im 2

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel

15 of 5 previous next Post a comment

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
  • @leminhhai that's cool :D
    Are you sure you want to
    Your message goes here
    Processing…
  • cũng không có biết anh Tuấn thuyết trình , nếu không ghé xem rùi
    Are you sure you want to
    Your message goes here
    Processing…
  • Về recommendation có thể tham khảo rất nhiều tài liệu hay từ http://www.netflixprize.com/ (Năm 2009 Netflix có tổ chức cuộc thi về recommendation, nếu nhóm nào tăng kết quả reommendation lên 10% thì được $1 triệu). Về tối ưu performance thì quả thực là vấn đề đau đầu, nếu có tiền thì dùng AMZ hay Google clould (+ sử dụng thêm Predition api). Có một bài viết khá hay về tính correlations 316 triệu phim của Bo Yang trong vòng 2 phút thay vì 2.5 giờ http://dmnewbie.blogspot.com/2009/06/calculating-316-million-movie.html . Một số recommendation service có thểm tham khảo là: http://www.recomaticapp.com/ http://monetate.com/ - expensive
    Are you sure you want to
    Your message goes here
    Processing…
  • kết thúc thiếu cái quan trọng là high performance thì làm như nào :D
    Are you sure you want to
    Your message goes here
    Processing…
  • Ôi, đợi cái Content-based filtering mà kết thúc cụt vậy
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    How to build a Recommender System How to build a Recommender System Presentation Transcript

    • 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/