Your SlideShare is downloading. ×
0
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Yossi cohen   3 base
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Yossi cohen 3 base

226

Published on

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
226
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Personalizationwith Big Data21/5/13.2013Yossi CohenCTO 3Base(Taldor Group)
  • 2. Who we are & what we do Projects in cloud environment SAAS, Multi tenant, lots of users >20 Big Data projects in various technologies(NoSQL, Hadoop etc.) End to end solution Architecture & design Implementation Maintenance2
  • 3. You must have seen this…3
  • 4. 4
  • 5. 5
  • 6. 6
  • 7. 7
  • 8. Some examples8
  • 9. The biggest one9
  • 10. Another one10
  • 11. Lessons Users have different tastes Users don’t have patience We need to show them things they like… Approaches Modeling Content Based Collaborative Filtering11
  • 12. Collaborative Filtering is … Given a user’spreferences for items,guess which otheritems would be highlypreferred Only needspreferences;users and itemsopaque Many algorithms!12
  • 13. Collaborative Filtering is …Sean likes “Scarface” a lotRobin likes “Scarface” somewhatGrant likes “The Notebook” not at all…(123,654,5.0)(789,654,3.0)(345,876,1.0)…(345,654,4.5)…(Magic)Grant may like “Scarface” quite a bit…13
  • 14. Recommending people food14
  • 15. Item-Based Algorithm Recommend items similar to a user’shighly-preferred items15
  • 16. Item-Based Algorithm Have user’s preference for items Know all items and can compute weightedaverage to estimate user’s preference What is the item – item similarity notion?for every item i that u has no preference for yetfor every item j that u has a preference forcompute a similarity s between i and jadd us preference for j, weighted by s,to a running averagereturn top items, ranked by weighted average16
  • 17. Item-Item Similarity Could be based on content… Two foods similar if both sweet, both cold BUT: In collaborative filtering, based only onpreferences (numbers) Pearson correlation between ratings ? Log-likelihood ratio ? Simple co-occurrence:Items similar when appearing often in the same user’s setof preferences17
  • 18. As matrix math User’s preferences are a vector Each dimension corresponds to one item Dimension value is the preference value Item-item co-occurrences are a matrix Row i / column j is count of item i / j co-occurrence Estimating preferences:co-occurrence matrix × preference (column) vector18
  • 19. As matrix math16 9 16 5 69 30 19 3 216 19 23 5 45 3 5 10 206 2 4 20 916 animals ate bothhot dogs and icecream10 animals ateblueberries05520135251220607019
  • 20. Hadoop way20
  • 21. Apache Mahout is … Machine learning … Collaborative filtering(recommenders) Clustering Classification Frequent item set mining and more … at scale Much implemented on Hadoop Efficient data structures21
  • 22. Thank you For more information:Yossi@3base.co.ilwww.taldor.co.il

×