The document discusses recommendation algorithms used by Amazon, including traditional collaborative filtering, cluster models, and search-based methods. It focuses on Amazon's item-to-item collaborative filtering algorithm. This algorithm builds a similar-items table offline by finding items customers tend to purchase together. It then scales well to large data sets, provides high quality recommendations even with limited user data, and performs recommendations quickly.

1. Amazon.com Recommendation Item-to-Item Collaborative Filtering Authors: Greg Linden,Brent Smith,and Jeremy York Origin: JANUARY • FEBRUARY 2003 Published by the IEEE Computer Society Reporter: 朱韋恩 Date: 2008/11/3

2. Outline Introduction Problems Recommendation Algorithms Comparison Conclusion

3. Recommender system in our life

4. Some problem Many applications require the results set to be returned in realtime New customers typically have extremely limited information Customer data is volatile

5. Three common approaches to solving the problem Traditional collaborative filtering Cluster models Search-based methods Amazon.com Item-to-Item CF Algorithm

6. Traditional Collaborative Filtering Nearest-Neighbor CF algorithm Cosine distance For N-dimensional vector of items, measure two customers A and B

7. Traditional Collaborative Filtering Disadvantage 1.examines only a small customer sample... 2.item-space partitioning ... 3.If discards the most popular or unpopular items...

8. Cluster Models Goal: Divide the customer base into many segments and assign the user to the segment containing the most similar customers

9. 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.

10. 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

11. 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

12. Search-Based Methods Disadvantage 1.too general 2.too narrow

13. 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

14. Amazon.com

15. Amazon.com

16. 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

17. Item-to-Item Collaborative Filtering Advantage Incerase the scalability and performance

18. 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

19. 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

20. Conclusion