Social Bookmarking and Collaborative Filtering

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Social Bookmarking and Collaborative Filtering

• Christopher G. Wagner

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What is Social Bookmarking?

• Bookmark storage
• Online storage vice locally in a browser
• No folders
• Items can belong to more than one folder
• Finding others with similar interests
• Using interests of others to locate more
  interesting sites

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Views of Social Bookmarks

• View personal bookmarks and tags
• View all items with a particular tag(s)
• New way of searching
• View tags of another user
• Create private and public groups for sharing
• View ratings of bookmarks

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Joshua Schacters del.icio.us
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Joshuas math Tag
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The math Tag
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The del.icio.us Interface
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My del.icio.us
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A del.icio.us Network
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The for Tag
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Social Bookmarking Projects

• Del.icio.us
• Furl.net
• Flickr.com
• Simpy.com
• Gmail.com
• Clusty.com
• Stumbleupon.com
• IBMs dogear

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What is Collaborative Filtering?

• Collaborative filtering (CF) is the method of
  making automatic predictions (filtering) about
  the interests of a user by collecting taste
  information from many users (collaborating).
  -Wikipedia (http//en.wikipedia.org/wiki/Collabor
  ative_filtering)
• Take advantage of users input and behavior to
  make recommendations.
• System for helping people find relevant content
• -Rashmi Sinha (http//www.rashmisinha.com)

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TraditionalCollaborative Filtering

• Each user represented by an N-dimensional vector,
  where N is the number of items
• Elements of vector can be ratings, or indicator
  of purchase, etc.
• Typically multiplied by the inverse frequency
• Use algorithm to measure similarity of vectors,
  e.g. cosine similarity

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Problems

• M customers, N items
• O(MN) is worst case
• Typically O(MN)
• Still problematic when M,N 106

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Cluster Models

• View customers as a classification problem
• Create clusters of customers
• Assign user to nearest cluster
• Base recommendations on users cluster

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Search Based Methods

• Construct searches based on keywords from users
  existing items
• Not practical if user has many items
• Recommendations tend to be poor

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Types ofCollaborative Filtering

• Active
• Sending pointers to a resource
• User ratings
• Passive
• Observing user behavior
• Item Based
• Items become the focus, not users

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Active Collaborative Filtering

• Uses a peer-to-peer approach
• Users want to actively share information,
  recommendations, evaluations, ratings, etc.
• Usually, information is from a user who has
  direct experience with the product
• Biased opinions
• Less data available

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Netflix Queue
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Netflix Ratings
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Netflix Recommendations
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Netflix Prize

• October 2, 2006 - October 2, 2011
• Improve their recommendation system by at least
  10 over the current method
• 1M Grand Prize
• 50k Yearly Prizes

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Passive Collaborative Filtering

• Monitor users activity
• Purchasing item
• Repeated use of an item
• Number of times queried
• Makes use of implicit filters
• Requires nothing additional from users
• Doesnt capture users evaluation

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Googles Sponsored Links

• www.AreYouASlackerMom.com
• www.royalsaharajasper.com
• Related to Pi Mu Epsilon
• Will pay stipend to Grad
• Cheap Faculty Flights
• Greek Ringtone

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Googles Personalized Search
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Item-to-ItemCollaborative Filtering

• Focus is on finding similar items, not similar
  customers
• Originally proposed by Vucetic and Obradovic in
  2000
• Matches users items to similar items to create
  recommendations
• Association Rule Mining

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Amazon Slide

• Similar to impulse items in checkout line
• Tailored to each user

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Amazons Recommendations
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Amazons Similar Items
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Amazons Algorithm

• For each item in product catalog, I1
• For each customer C who purchased I1
• For each item I2 purchased by customer C
• Record that a customer purchased I1 and I2
• For each item I2
• Compute the similarity between I1 and I2
• Only items purchased by common customer are
  compared, not all pairs of items

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Run Time of Algorithm

• Worst case O(N2M)
• In practice, more like O(NM)
• Is run offline, so it does not affect customer
• For customer, you only have to aggregate items
  similar to their purchases and make
  recommendations, which is fast

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Collaborative FilteringWith Tags

• User input is usually a barrier, not so with tags
• Users bookmarks reveal information about their
  interests, which is useful for finding others of
  similar interests
• Applications to corporate repositories of
  information (IBMs dogear)
• Both active (tags) and passive (logs) filtering

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References

• G. Linden, B. Smith, and J. York, Amazon.com
  Recommendations Item-to-Item Collaborative
  Filtering, IEEE Internet Computing, 2003, pp.
  76-80.
• R. Sinha, Collaborative Filtering strikes back
  (this time with tags), http//www.rashmisinha.com
  /archives/05_10/tags-collaborative-filtering.html.
  
• S. Vucetic and Z. Obradovic, A Regression-Based
  Approach for Scaling-Up Personalized Recommender
  Systems in E-Commerce, Workshop on Web Mining
  for E-Commerce, at the 6th ACM SIGKDD Intl Conf.
  on Knowledge Discovery and Data Mining (KDD),
  Boston, MA, 2000.
• R. Wash and E. Rader, Collaborative Filtering
  with del.icio.us, working paper.
• R. Wash and E. Rader, Incentives for
  Contribution in del.icio.us The Role of Tagging
  in Information Discovery, working paper.
• Wikipedia, Collaborative Filtering,
  http//en.wikipedia.org/wiki/Collaborative_filteri
  ng.