1. Recommender Systems and Collaborative Filtering
Drawing much on some online ppt in this area, especially William W. Cohen (CMU)

2. You visit an online bookshop ...
The shop has 100,000 books. On the webpage, they will display 5 book covers, especially for you. What ones will they display?

3. Why?
same for books, webpages, music, films, clothes, food, everything ... this is very serious for e-commerce -- big financial uplift if stores get recommendations ‘right’
What if the website is not selling you anything (e.g. research papers, search, interest group forum). Why does such a site need to make good recommendations?

4. Basic approaches used for recommendation
User-based
Recommend things that were purchased or viewed by users who are similar to you
Item-based
Recommend things that are similar to the items that you have viewed/purchased before

5. Amazon: ‘cold-start’ recomendation

6. Amazon: with minimal info about me via a cookie on this netbook

7. Amazon, when I logged in

8. User Profiles
For user-based recommendation, sites need to have some kind of user profile. Similarity with other users is based on distance measurements based on the profile. What do you think could be in a user profile?

9. Potential contents of user profiles
Demographic data: age, gender, salary, profession, country of residence, country of origin, religion ...
Site behaviour: Purchase history at the site; viewing history, perhaps including time spent on certain pages/items; clickstream sequence

10. K-Nearest Neighbour based Recommendation
Age
You
Salary
(Think in terms of many dimensions, not just these two)

11. K-Nearest Neighbour based Recommendation
Age
You
Salary
Your neighbours: recommend things that they have viewed/purchased

12. Collaborative Filtering: The main idea
People who purchased A also purchased B
Different from nearest-neighbour; this can lead
to recommendations based on behaviour of
users who are very dissimilar to you

13. Other forms/aspects of collaborative filtering
Why “collaborative”? Basically, someone else (in fact many someones) have gone to the effort of viewing/filtering things, and chosen the best few. You get a recommendation of the best few, without having to spend the effort. Rampant examples of CF: twitter, pagerank, stumbleupon, digg, Facebook (Likes), etc ...

14. Another look at Google’s PageRank (this bit adapted from slides of William Cohen, CMU)
Inlinks are “good” (recommendations)
Inlinks from a “good” site are better than inlinks from a “bad” site
but inlinks from sites with many outlinks are not as “good”...
“Good” and “bad” are relative.
web site xxx
web site xxx
web site xxx
web site a b c d e f g
web
site
pdq pdq ..
web site yyyy
web site a b c d e f g
web site yyyy

15. Google’s PageRank (Brin & Page, http://www-db. stanford
web site xxx
Imagine a “pagehopper” that always either
follows a random link, or
jumps to random page
PageRank ranks pages by the amount of time the pagehopper spends on a page:
or, if there were many pagehoppers, PageRank is the expected “crowd size”
web site xxx
web site a b c d e f g
web
site
pdq pdq ..
web site yyyy
web site a b c d e f g
web site yyyy

16. Collaborative Filtering and User Ratings
Many systems ask users to rate items – e.g. on a scale of
1 to These ratings then enable the system to give more
precise/accurate recommendations, and use a variety of
sophisticated learning/prediction algorithms.

17. Collaborative Filtering and User Ratings
Many systems ask users to rate items – e.g. on a scale of
1 to These ratings then enable the system to give more
precise/accurate recommendations, and use a variety of
sophisticated learning/prediction algorithms.
E.g. Here are user ratings for some items: “?” means unrated.
A B C D E F G H
You: ? ?
User ?
User
User ?
How might a system predict your rating for items G and H?

18. Collaborative Filtering Works

19. BellCore’s MovieRecommender (Bell Communications Research)
Participants sent to
System replied with a list of 500 movies to rate on a 1-10 scale (250 random, 250 popular)
Only subset need to be rated
New participant P sends in rated movies via
System compares ratings for P to ratings of (a random sample of) previous users
Most similar users are used to predict scores for unrated movies
System returns recommendations in an message.

21. Start your own business? Bookmark based recommendation

22. Display the right adverts on your site

23. End