Recommender Systems and Collaborative Filtering

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Recommender Systems and Collaborative Filtering

• Drawing much on some online ppt in this area,
  especially William W. Cohen (CMU)

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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?

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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?

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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

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Amazon cold-start recomendation
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Amazon with minimal info about me via a cookie
on this netbook
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Amazon, when I logged in
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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?

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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

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K-Nearest Neighbour based Recommendation
Age
You
Salary
(Think in terms of many dimensions, not just
these two)
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K-Nearest Neighbour based Recommendation
Age
You
Salary
Your neighbours recommend things that they
have viewed/purchased
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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
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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 ...

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Another look at Googles 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
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Googles PageRank(Brin Page,
http//www-db.stanford.edu/backrub/google.html)
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
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Collaborative Filtering and User Ratings
Many systems ask users to rate items e.g. on a
scale of 1 to 10. These ratings then enable the
system to give more precise/accurate
recommendations, and use a variety of
sophisticated learning/prediction algorithms.
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Collaborative Filtering and User Ratings
Many systems ask users to rate items e.g. on a
scale of 1 to 10. 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 7
2 1 8 9 9 ? ? User1
1 8 8 2 ? 2 8
7 User2 6 3 3 7 6 5
3 1 User3 7 2 1 7 7
? 3 1 How might a system predict your
rating for items G and H?
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Collaborative Filtering Works
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BellCores MovieRecommender(Bell Communications
Research)

• Participants sent email to videos_at_bellcore.com
• 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 email
• 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 email
  message.

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Start your own business? Bookmark based
recommendation
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Display the right adverts on your site
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End