Social Bookmarking and Collaborative Filtering

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Social Bookmarking and Collaborative FilteringChristopher G. Wagner

+ 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

+ 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

+ Joshua Schacter’s del.icio.us

+ Joshua’s ‘math’ Tag

+ The ‘math’ Tag

+ The del.icio.us Interface

+ My del.icio.us

+ A del.icio.us Network

+ The ‘for:’ Tag

+ Social Bookmarking Projects Del.icio.us

Furl.net

Flickr.com

Simpy.com

Gmail.com

Clusty.com

Stumbleupon.com

IBM’s dogear

+ 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/Collaborative_filtering)

Take advantage of users’ input and behavior to make recommendations.

“System for helping people find relevant content”

-Rashmi Sinha (http://www.rashmisinha.com)

+ 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

€

cos(A,B) = A • BA × B

+ Problems

M customers, N items

O(MN) is worst case

Typically O(M+N) Still problematic when M,N ~ 106

+ Cluster Models

View customers as a classification problem Create clusters of customers Assign user to “nearest” cluster Base recommendations on user’s cluster

+ Search Based Methods

Construct searches based on keywords from user’s existing items

Not practical if user has many items

Recommendations tend to be poor

+ Types ofCollaborative Filtering Active

Sending pointers to a resource User ratings

Passive Observing user behavior

Item Based Items become the focus, not users

+ 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

+ Netflix Queue

+ Netflix Ratings

+ Netflix Recommendations

+ 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

+ Passive Collaborative Filtering Monitor user’s activity

Purchasing item Repeated use of an item Number of times queried

Makes use of implicit filters Requires nothing additional from users Doesn’t capture user’s evaluation

+ Google’s Sponsored Links

www.AreYouASlackerMom.com

www.royalsaharajasper.com

Related to Pi Mu Epsilon “Will pay stipend to Grad” “Cheap Faculty Flights” “Greek Ringtone”

+ Google’s Personalized Search

+ Item-to-ItemCollaborative Filtering•Focus is on finding similar items, not similar customers

•Originally proposed by Vucetic and Obradovic in 2000

•Matches user’s items to similar items to create recommendations

•Association Rule Mining

+ Amazon Slide

Similar to impulse items in checkout line Tailored to each user

+ Amazon’s Recommendations

+ Amazon’s Similar Items

+ Amazon’s 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

+ 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

+ Collaborative FilteringWith Tags User input is usually a barrier, not so with tags

User’s bookmarks reveal information about their interests, which is useful for finding others of similar interests

Applications to corporate repositories of information (IBM’s dogear) Both active (tags) and passive (logs) filtering

+ 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 Int’l 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_filtering.