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Amazon.com RecommendationsItem-to-Item Collaborative Filtering
CIS 601: Graduate Seminar
Prof. S. S. Chung
Presented By:-
Amol Chaudhari
CSU ID 2682329
Outline
� Introduction
� Problems
� RecommendationAlgorithms
� Comparison
� Conclusion
Introduction
Introduction
� Best for use in e-Commerce websites.
� Input – Customers Interest.
� Output-List of recommendation.
� Attributes like items viewed, demographic data, subject interest etc.
Challenges for algorithm :
� Retailer with huge amount of data, catalogue andmillions of customers
� Real-time output should be in no more than halfsecond with high quality results.
� New customers have limited info about product
� Older customer with huge Information.
� New data gets added rapidly. Customers data isVolatile
Three common approaches to solving the problem� Traditional collaborative filtering
� Cluster models
� Search-based methods
Traditional Collaborative Filtering
� Represent a customer as an N-dimentional vector of items
� Typically multiplies the vector component by inverse frequency.
� Generates recommendations based on few customers who are most similar to the user.
Traditional Collaborative Filtering
Disadvantage:� examines only a small customer sample...
� If most popular or unpopular items are discarded they will never appear as recommendation.
� Very expensive and time complexity is O(MN) for worst case and for best case O(M+N)
Cluster Models
� Divide the customer base into manysegments and assign the user to the segment containing the most similar customers
� Segments created using clustering.
� Groups similar customers
� Any clustering or learning algorithm can be used.
Cluster Models
Advantages:-
� better onlinescalability and performance than Collaborative
Disadvantages:-
� The recommendation quality is low when it runs offline.
� Equal expensive to collaborative
Search Based Methods
� Content based method
� Constructs a search query , find other popular items by similarity.
� Impractical to base query on all items.
� If the user has few purchases or ratings,search- based
recommendation algorithms scale and perform well
Disadvantages:� too general
� too narrow
Item-to-Item Collaborative Filtering
� Amazon uses recommendations as a targeted marketing tool in many email campaign and web site pages.
� Eg. “your recommendations”
Item-to-Item Collaborative Filtering
How it Works
� Instead of matching the user to similar customer , it matches each of the users purchased items.
� Combine items to make list.
� Similar Item table which are purchased together.
� Build product to product matrix by iterating through all item pairs and similarity metric.
Item-to-Item Collaborative Filtering
� Algorithm
� Increase the scalability and performance
Scalability: A Comparison
Traditional Collaborative filtering:� Online computation , � Impractical on large dataset, unless dimensionality
reduction , sampling , and partitioning is one.
Cluster models:� Offline computation� Quality is poor� Increase number of segments to improve quality but cost
increases.
Search based Models:� Build keyword, category author indexes offline , but no
recommendation as per interest titles.� Scale poorly for customers with numerouspurchases and
ratings
Scalability: A Comparison
Item-to-item collaborative filtering:
� fast for extremely large data set
� creates the similar-items table offline
� performs well with limited userdata
� quality is excellent
Conclusion:
� Recommendation algorithm provides an effective form of targeted marketing by creating personalized experience for each customer.
� Large retailers like Amazon it is scalable over larger dataset.
� It takes only few sub seconds processing time to generate online recommendation.
� In future it is expected that retail industry will use this algorithm for targeted marketing , both offline and online.
THANK YOU