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SIGIR'22 "Microsoft" CTR estimation: using context information to promote feature representation learning
2022-04-23 20:11:00 【Zhiyuan community】
Paper title :
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
Thesis link :
https://arxiv.org/pdf/2204.08758.pdf
code:
https://github.com/frnetnetwork/frnet
In this paper, we consider , Feature representation and context (context) The relationship between , Feature refinement network is proposed FRNet, The module learns bit levels for each feature in different contexts (bit-level) Context aware feature representation .FRNet It consists of two key components :
-
1) Information extraction unit (IEU), It captures contextual information and cross feature relationships , To guide the feature refinement of context awareness ; -
2) Complementary selection gate (CSGate), It adaptively will be in IEU The original and complementary feature representation of learning is combined with bit level weight .
FRNet It's a module , Can be compared with other ctr Model combination to improve performance . about CTR The basic process of the base model will not be repeated here , If you want to know more, you can go to the third chapter of the paper to read .
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https://yzsam.com/2022/04/202204232004206710.html
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