recommendation systems

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.

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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/