孙超 - Recommendation Algorithm as a product
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Transcript of 孙超 - Recommendation Algorithm as a product
Abstract
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.
Summary
How to display the algorithm The relationship between real products
and recommended algorithm Algorithm’s algorithm
Algorithm as implicit product
Algorithm as implicit product
algo1 algo2 algo3
algo4 algo5 algo6
Why not think about…? Think about hybrid algorithm? Dose the customer like our algorithm? The personalize algorithm? The algorithm’s algorithm?
How to produce
1) different model 2) different dataset 3) different parameter 4) different algorithm 5) hybrid algorithm
Algorithm’s algorithm
K-Nearest Neighbor algorithm(knn)
AprioriContent-basedUser-basedItem-basedVector cosine
The relationship
Dataset
Team1
Team2
Team3
Product1
Product2
Product3
Product4
Product5
Product6
Product7
Product8
UserA
UserB
DatasetUser 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
DatasetUser 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)
Binary datasetuser_ba
seitem_bas
econten_b
aseother 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
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
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
Item baseItem 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
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
AprioriK-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
End