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Development trend of Ali Taobao fine sorting model
2022-07-06 01:06:00 【learner_ ctr】
One 、DIN The Internet 2018 year 7 month 19 Japan
, Put forward attention Structure is used for user sequence characteristics , such attention No self-attention, It's an estimate item Used as query, Each in the user sequence item As key value
Two 、DIEN The Internet 2018 year
be relative to DIN The network has such improvements ,DIN The network only makes user sequence attention This matter ,DIEN First, add a loss, use gru Calculate the click sequence ( The length is T) Of T Outputs , Then take the front. T-1 And output after T-1 Seek accumulation loss, In this way, this prediction will include the significance of predicting the next .
For this T Output using tape attention Of gru Get the final output final_state2, And other features (user Of embedding、item Of embedding、 Various sequences 、 Dot product of the first two )concat together , Send it to the back mlp layer
dien Thesis translation _1066196847 The blog of -CSDN Blog
3、 ... and 、 Long sequence modeling MIMN 2019 year 7 month 25 Japan
It's solved DIN DIEN It can't deal with the bottleneck of long sequence , This model can handle lengths up to 1000 Sequence modeling
Modeling the long-term historical user behavior of Alibaba mom ——MIMN The model, - You know
Four 、sim Model 2020 year 6 month
MIMN Ask for more , No consideration target item;din Be accurate , But it can't handle long sequences ,sim The model can handle lengths up to 10000 Sequence , This is in the e-commerce scene ( TaoBao ) Or content scene ( Tiktok ), It's very common , For example, I learned from the article ,180 Day behavior data 30% The click sequence of samples exceeds 10000, This proportion is quite a lot
sim First, search out a batch of suitable item, recycling din Thoughts are processed
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