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Recommendation Systems. By: Bryan Powell, Neil Kumar, Manjap Singh. R ecommendation system?. Information filtering technology Presents data on products that interests the user Algorithm uses previous user interactions. Recommendation System. Observes apparent user characteristics - PowerPoint PPT Presentation
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Recommendation Systems
By: Bryan Powell, Neil Kumar, Manjap Singh
Recommendation system?• Information filtering technology• Presents data on products that interests
the user• Algorithm uses previous user
interactionsRecommendation
System
What does a
recommendation
system do
exactly?
Observes apparent user characteristics
Compares characteristics to an item
Predicts a rating the user would give to
the item
Assigns the highest predicted item as a
recommendation
General Recommendation Types • Personalized
recommendation • based on the individual's
past behavior
• Social recommendation • based on the past
behavior of similar users
• Item recommendation• based on the item itself
Amazon
• Amazon used all 3 approaches (personalized, social and item).
• Amazon’s recommendation system is very sophisticated
ALL MIGHTY GOOGLE• Google uses its recommendation system
every time a user searches through it.
• Based on your location and/or recent search activity
• When you're signed in to your Google Account, you “may see even more relevant, useful results based on your web history”
Google Cont.• Google's search algorithm is called
PageRank.• Dependent on social recommendations
(i.e. who links to a webpage)
• Google also does item recommendations with its “Did you mean” feature.
Who uses Recommendation
Systems?• Content Sites• eCommerce Sites• Advertisment
Content Sites• Task:
• predict ratings of items by a given user • find a list of interesting items
• Data: • content description• explicit rating for some user
• Examples: AlloCine, Zagat, LibraryThing, Last.fm, Pandora, StumbleUpon
Recommendation for a user on LibraryThing
eCommerce Sites• Task:
• build group of products for bundle sales • find a list of products that a user is likely to buy
• Data: • list of purchases • browsing history for all users
• Example: • Amazon• Netfix
The Recommendation Gianthttp://www.netflix.com/
eCommerce Sites Cont.Netflix Prize• $1 million prize
given in 2009• Sought to
substantially improve Netflix’s method of predictions for users
eCommerce Sites Cont.
Netflix Challenge Cont.
The BellKor’s Pragmatic Chaos team improved Netflix’s recommendation system by 10.06 %
BellKor's Pragmatic
Chaos
eCommerce Sites Cont.(Netflix Cont.)
The BellKor’s Pragmatic Chaos team had a lower score than the 2nd place team (The Ensemble)
The Belkor’s Pragmatic Chaos team: (10.06%) The Ensemble: (10.06%)
The Belkor’s Pragmatic Chaos only won because they submitted their code 20 minutes before The Ensemble.
.856714
.856714
Advertisement• Task:
• find a list of advertisements optimized according to expected income
• Data: browsing history for all users
• Example: Google AdSense, DoubleClick
Common Approaches to Recommendation Systems
Content Filtering Algorithms
Collaborative Filtering Algorithms
Hybrid Methods
K-Nearest Neighbor Approach
Content Filtering Algorithms• Algorithm based on attributes of
items and ratings of the user
• Interprets the preferences of a user as a function of attributes
• Two main types of C.F.A.:• Heuristic – Based• Model Based
Content Filtering Algorithms Cont.• Heuristic Based• Uses common types of information
retrieval • TF/ ID• Cosine• Clustering
• Model Based• Uses a probabilistic model to learn the
predictions of a user
Collaborative Filtering • Filters information/patterns using different
sources
• Involves very large data sets
• Filters what the user sees based on tastes
• Steps:• Look for users who share similar rating
patterns• Calculate predictions for user from other
ratings
• Amazon invented item-based collaborative filtering
Collaborative Filtering Cont.
Hybrid Methods• Uses both item attributes and the
ratings of all users
• Hybrid methods were made to cope with the conventional recommendation system
• Two main types of C.F.A.:• Heuristic – Based• Model Based
Hybrid Methods Cont.• Heuristic Based• Uses both content filtering and
collaborative filtering methods• Aims to get the best from both
algorithms
• Model Based• Model is modified in order to take into
account both types of data
K-Nearest Neighbor Approach• Classified based on a majority of its neighbors
• Classifies Objects based on closest training examples
• Computation deferred until classification instance-based learning
• Can be used for regression and utilizes Euclidean distances
• Larger “k” values reduce noise on classification • They make boundaries between classification
less distinct
Additional ResourcesNetflix Prize-
http://www.netflixprize.com//community/viewtopic.php?id=1537
uPenn- http://www.cis.upenn.edu/~ungar/CF/