amazon item-to-item recommendations
TRANSCRIPT
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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
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Outline
Introduction Problems Recommendation Algorithms Comparison Conclusion
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Recommender system in our life
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Some problem
Many applications require the results set to be returned in realtime
New customers typically have extremely limited information
Customer data is volatile
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Three common approaches to solving the problem
Traditional collaborative filtering Cluster models Search-based methods
Amazon.com Item-to-Item CF Algorithm
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Traditional Collaborative Filtering
Nearest-Neighbor CF algorithm Cosine distance
For N-dimensional vector of items, measure two customers A and B
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Traditional Collaborative Filtering
Disadvantage 1.examines only a small customer sample... 2.item-space partitioning ...
3.If discards the most popular or unpopular items...
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Cluster Models
Goal: Divide the customer base into many
segments and assign the user to the segment containing the most similar customers
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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.
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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
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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
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Search-Based Methods
Disadvantage
1.too general 2.too narrow
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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
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Amazon.com
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Amazon.com
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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
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Item-to-Item Collaborative Filtering
Advantage Incerase the scalability and performance
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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
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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
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Conclusion