Amazon.com RecommendationItem-to-Item Collaborative Filtering

Authors:Greg Linden,Brent Smith,and Jeremy YorkOrigin:JANUARY • FEBRUARY 2003 Published by the IEEE Computer SocietyReporter:朱韋恩Date:2008/11/3

Outline

Introduction Problems Recommendation Algorithms Comparison Conclusion

Recommender system in our life

Some problem

Many applications require the results set to be returned in realtime

New customers typically have extremely limited information

Customer data is volatile

Three common approaches to solving the problem

Traditional collaborative filtering Cluster models Search-based methods

Amazon.com Item-to-Item CF Algorithm

Traditional Collaborative Filtering

Nearest-Neighbor CF algorithm Cosine distance

For N-dimensional vector of items, measure two customers A and B

Traditional Collaborative Filtering

Disadvantage 1.examines only a small customer sample... 2.item-space partitioning ...

3.If discards the most popular or unpopular items...

Cluster Models

Goal: Divide the customer base into many

segments and assign the user to the segment containing the most similar customers

Cluster Models

Advantage in smaller size of group have better online

scalability and performance

Disadvantage complex and expensive clustering

computation is run offline. However, recommendation quality is low.

Search-Based Methods

Given the user’s purchased and rated items, constructs a search query to find other popular items

For example, same author, artist, director, or similar keywords

Search-Based Methods

If the user has few purchases or ratings, search-based recommendation algorithms scale and perform well

If users with thousands of purchases, it is impractical to base a query on all the

items

Search-Based Methods

Disadvantage

1.too general 2.too narrow

Item-to-Item Collaborative Filtering

Rather than matching the user to similar customers, build a similar-items table by finding that customers tend to purchase together

Amazon.com used this method

Amazon.com

Amazon.com

Item-to-Item CF 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

Item-to-Item Collaborative Filtering

Advantage Incerase the scalability and performance

Scalability: A Comparison

Traditional CF: Impractical on large data sets Cluster models: Perform much of the computation offline,

but recommendation quality is relatively poor

Search-based models: Scale poorly for customers with numerous

purchases and ratings

Scalability: A Comparison

Item-to-Item CF: -creates the similar-items table offline -fast for extremely large data set -quality is excellent -performs well with limited user data

Conclusion