Recommendation algorithm is a product 孙超 刘凯义 [email_address]
Abstract <ul><li>In most cases, we find the similarity between two users depend on the preference of items.  But in some c...
Summary <ul><li>How to display the algorithm </li></ul><ul><li>The relationship between real products and recommended algo...
Algorithm as implicit  product
Algorithm as implicit  product algo1 algo2 algo3 algo4 algo5 algo6
Why not think about…? <ul><li>Think about  hybrid  algorithm? </li></ul><ul><li>Dose the customer like our algorithm? </li...
How to produce  <ul><li>1) different model </li></ul><ul><li>2) different dataset </li></ul><ul><li>3) different parameter...
Algorithm’s algorithm <ul><li>K-Nearest Neighbor algorithm(knn) </li></ul><ul><li>Apriori </li></ul><ul><li>Content-based ...
The relationship Dataset Team1 Team2 Team3 Product7 Product8 UserA UserB Product1 Product2 Product3 Product4 Product5 Prod...
Dataset User id Item id Time Algo  id 4027065 10310198 2009-11-30 23:49:07 100025 4027065 10882081 2009-11-30 23:52:48 101...
Dataset User id Item id Time Algo id 1(Bob) 1( 青花瓷 ) 2009-11-30 23:49:07 1(user_base) 1(Bob) 2( 十年 ) 2009-11-30 23:52:48 1...
Binary dataset user_base item_base conten_base other apriori Bob 1 0 0 0 0 Linda 1 1 0 0 0 Lucy 0 0 1 1 1 Tom 0 1 0 0 1 Pe...
Dispatcher User ID  Other user ID Algorithm  Similarity Bob Linda 0.589723 Bob Tom 0.279055 Linda Tom 0.279055 Lucy Tom 0....
User base  User id  User  id  Similarity Bob Linda 0.279055 Bob Lucy 0.416997 Linda Lucy 0.197322 Linda Tom 0.310667 Linda...
Item base Item id  Item id  Similarity 青花瓷 十年 0.463457 青花瓷 富士山下 0.256949 青花瓷 天黑黑 0.256949 十年 富士山下 0.209798 十年 天黑黑 0.209798...
Content base 青花瓷 菊花台 晴天 七里香 十年 K 歌之王 背包 从何说起 富士山下 爱情转移 好久不见 在你身边 天黑黑 遇见 开始懂了 花木兰 双截棍 稻香 断了的弦 霍元甲 木兰情 天黑黑 爱情证书 原点
Apriori K-Items Min-Sup Confidence 青花瓷 , 十年 30% 青花瓷  十年  =100% 青花瓷 , 十年 30% 十年  青花瓷  = 66.7%
Other 一直走 张倩 带我飞 林志玲 叹金莲 阿朵 把握你的美 江映蓉 看月亮爬上来 张杰
Data stream
<ul><li>End </li></ul>
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孙超 - Recommendation Algorithm as a product

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孙超 - Recommendation Algorithm as a product

  1. 1. Recommendation algorithm is a product 孙超 刘凯义 [email_address]
  2. 2. Abstract <ul><li>In most cases, we find the similarity between two users depend on the preference of items. But in some cases, we can define the similarity by the preference of different recommended way, and also different algorithm. </li></ul>
  3. 3. Summary <ul><li>How to display the algorithm </li></ul><ul><li>The relationship between real products and recommended algorithm </li></ul><ul><li>Algorithm’s algorithm </li></ul>
  4. 4. Algorithm as implicit product
  5. 5. Algorithm as implicit product algo1 algo2 algo3 algo4 algo5 algo6
  6. 6. Why not think about…? <ul><li>Think about hybrid algorithm? </li></ul><ul><li>Dose the customer like our algorithm? </li></ul><ul><li>The personalize algorithm? </li></ul><ul><li>The algorithm’s algorithm? </li></ul>
  7. 7. How to produce <ul><li>1) different model </li></ul><ul><li>2) different dataset </li></ul><ul><li>3) different parameter </li></ul><ul><li>4) different algorithm </li></ul><ul><li>5) hybrid algorithm </li></ul>
  8. 8. Algorithm’s algorithm <ul><li>K-Nearest Neighbor algorithm(knn) </li></ul><ul><li>Apriori </li></ul><ul><li>Content-based </li></ul><ul><li>User-based </li></ul><ul><li>Item-based </li></ul><ul><li>Vector cosine </li></ul>
  9. 9. The relationship Dataset Team1 Team2 Team3 Product7 Product8 UserA UserB Product1 Product2 Product3 Product4 Product5 Product6
  10. 10. Dataset User id Item id Time Algo id 4027065 10310198 2009-11-30 23:49:07 100025 4027065 10882081 2009-11-30 23:52:48 101025 3292669 10814423 2009-11-30 23:00:43 101025 3292669 10026349 2009-11-30 23:05:43 200003 3765231 10896495 2009-11-30 23:39:01 102175 3765231 10023192 2009-11-30 23:14:34 200503 3765231 10018038 2009-11-30 23:04:53 201801 3977917 10023488 2009-11-30 23:46:24 102175 4008825 10093427 2009-11-30 23:28:28 102175 4008825 10031710 2009-11-30 23:16:29 201801 4010098 10300130 2009-11-30 23:20:44 200003 4010098 10320031 2009-11-30 23:20:46 200003
  11. 11. Dataset User id Item id Time Algo id 1(Bob) 1( 青花瓷 ) 2009-11-30 23:49:07 1(user_base) 1(Bob) 2( 十年 ) 2009-11-30 23:52:48 1(user_base) 2(Linda) 2( 十年 ) 2009-11-30 23:00:43 1(user_base) 2(Linda) 5( 双截棍 ) 2009-11-30 23:05:43 2(item_base) 3(Lucy) 1( 青花瓷 ) 2009-11-30 23:39:01 3(conten_base) 3(Lucy) 3( 富士山下 ) 2009-11-30 23:14:34 4(apriori) 3(Lucy) 2( 十年 ) 2009-11-30 23:04:53 5(other) 3(Lucy) 4( 天黑黑 ) 2009-11-30 23:46:24 3(conten_base) 4(Tom) 4( 天黑黑 ) 2009-11-30 23:28:28 2(item_base) 4(Tom) 5( 双截棍 ) 2009-11-30 23:16:29 4(apriori) 5(Peter) 6( 花木兰 ) 2009-11-30 23:20:44 5(other) 5(Peter) 3( 富士山下 ) 2009-11-30 23:20:46 4(apriori)
  12. 12. Binary dataset user_base item_base conten_base other apriori Bob 1 0 0 0 0 Linda 1 1 0 0 0 Lucy 0 0 1 1 1 Tom 0 1 0 0 1 Peter 0 0 0 1 1
  13. 13. Dispatcher User ID Other user ID Algorithm Similarity Bob Linda 0.589723 Bob Tom 0.279055 Linda Tom 0.279055 Lucy Tom 0.227848 Lucy Peter 0.481507 Tom Peter 0.279055
  14. 14. User base User id User id Similarity Bob Linda 0.279055 Bob Lucy 0.416997 Linda Lucy 0.197322 Linda Tom 0.310667 Linda Tom 0.219675 Lucy Peter 0.219675
  15. 15. Item base Item id Item id Similarity 青花瓷 十年 0.463457 青花瓷 富士山下 0.256949 青花瓷 天黑黑 0.256949 十年 富士山下 0.209798 十年 天黑黑 0.209798 十年 双截棍 0.253659 富士山下 天黑黑 0.256949 富士山下 花木兰 0.43935 天黑黑 双截棍 0.310667
  16. 16. Content base 青花瓷 菊花台 晴天 七里香 十年 K 歌之王 背包 从何说起 富士山下 爱情转移 好久不见 在你身边 天黑黑 遇见 开始懂了 花木兰 双截棍 稻香 断了的弦 霍元甲 木兰情 天黑黑 爱情证书 原点
  17. 17. Apriori K-Items Min-Sup Confidence 青花瓷 , 十年 30% 青花瓷  十年 =100% 青花瓷 , 十年 30% 十年  青花瓷 = 66.7%
  18. 18. Other 一直走 张倩 带我飞 林志玲 叹金莲 阿朵 把握你的美 江映蓉 看月亮爬上来 张杰
  19. 19. Data stream
  20. 20. <ul><li>End </li></ul>

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