1. Sean Blong Presents: 1

2. What are they…?  “[…] specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages, etc.) that are likely of interest to the user.”  More simply, enhances companies profits as well as the user’s shopping experience. (win-win) 2

3. Who uses them…? 3

4. So why should you care?  Netflix Prize Open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings Prize: $1,000,000 Biggest competitive advantage 4

5. 3 Types of Recommendation  Personalized recommendation  Social recommendation  Item recommendation 5

6. Personalized Recommendation  Recommend items based on the individual's past behavior.  Examples: Pandora Netflix Google 6

7. Netflix (Personalized Recommendation) 7

8. Social Recommendations  Recommend items based on the past behavior of similar users  Examples: Facebook friend recommendations Amazon Netflix 8

9. Amazon (Social Recommendation) 9

10. Item Recommendation  Recommend things based on the item itself  Examples: Amazon Most clothing companies Pandora 10

11. Urban Outfitters (Item Recommendation) 11

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13.  Google customizes your search results based on your location and/or recent search activity.  When signed in to a Google Account, you will see even more relevant results based on your web history. 13

14.  Google's search algorithm PageRank is dependent on social recommendations (who links to a webpage)  Google also does item recommendations with its "Did you mean" feature. Try typing recursion in the search bar. 14

15.  Pandora relies on a Music Genome that consists of 400 musical attributes covering the qualities of melody, harmony, rhythm, form, composition and lyrics.  Item based recommendations based on these musical attributes.  Not a social recommendation system!!! 15

16.  Combines all 3 techniques: All recommendations are based on individual behavior, the item itself, and the behavior of other people on Amazon. 16

17. 17  Item/Social Recommendation Personal/Item Recommendation 

18. How it applies to Advanced Clothing Solutions… 18

19. Goals:  Store user data: What they’ve bought/own, what they’ve tried on, what they like/don’t like.  Make recommendations: Utilizing the Item, Social, and Personal Recommendation systems.  Utilize data to create personalized sales, deals, and coupons. i.e. Increase profits and shopping experience! 19

20. Challenges:  THE ALGORTHIM How to assign similarity through tags? ○ How to assign tags? (see ER diagram) How to assign individual weights of the three recommendation facets (personal, social, item). How to accurately portray user’s tastes using a binary ranking system (think Pandora) 20

21. A Look at the Database… 21

22. More simply…  User: username, userid, name, address, phone  Article: articleid, type, gender, color, size, description, company name  Likes: userid, articleid, rating So what’s the issue…? 22

23. Problems with Social Recommendation vs. Personal Recommendation 23 Social: User: Social: User:

24. The technical side of Recommendation Systems… 24  Latent Factor (matrix factorization) vs. Nearest Neighbor Latent Factor: become popular in recent years by combining good scalability with predictive accuracy. In addition, they offer much flexibility for modeling various real- life situations.

25. Nearest Neighbor 25

26. Latent Factor 26

27. Matrix Factorization (cont.)  Other items to consider: Adding biases Additional input sources Temporal dynamics Inputs with varying confidence levels 27

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