update

Author: tangxyw

Industry/FeatureHashing/Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction.pdf

@@ -2,7 +2,7 @@
 # 推荐系统相关论文汇总
 ([English Version is Here](/README_EN.md))
 ## 介绍
-1. 截至2023-02-05，本仓库收集汇总了推荐系统领域相关论文共**553**篇，涉及：**召回**，**粗排**，**精排**，**重排**，**多任务**，**多场景**，**多模态**，**冷启动**，**校准**，
+1. 截至2023-02-12，本仓库收集汇总了推荐系统领域相关论文共**556**篇，涉及：**召回**，**粗排**，**精排**，**重排**，**多任务**，**多场景**，**多模态**，**冷启动**，**校准**，
 **纠偏**，**多样性**，**公平性**，**反馈延迟**，**蒸馏**，**对比学习**，**因果推断**，**Look-Alike**，**Learning-to-Rank**，**强化学习**等领域，本仓库会跟踪业界进展，持续更新。
 2. 因文件名特殊字符的限制，故论文title中所有的`:`都改为了`-`，检索时请注意。
 3. 文件名前缀中带有`[]`的，表明本人已经通读过，第一个`[]`中为论文年份，第二个`[]`中为发表机构或公司(可选)，第三个`[]`中为论文提出的model或method的简称(可选)。
@@ -197,6 +197,7 @@
 - [[2022][Pinterest][PinnerFormer] PinnerFormer - Sequence Modeling for User Representation at Pinterest](Industry/Representation/%5B2022%5D%5BPinterest%5D%5BPinnerFormer%5D%20PinnerFormer%20-%20Sequence%20Modeling%20for%20User%20Representation%20at%20Pinterest.pdf)
 #### FeatureHashing
 - [[2021][Google][DHE] Learning to Embed Categorical Features without Embedding Tables for Recommendation](Industry/FeatureHashing/%5B2021%5D%5BGoogle%5D%5BDHE%5D%20Learning%20to%20Embed%20Categorical%20Features%20without%20Embedding%20Tables%20for%20Recommendation.pdf)
+- [Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction](Industry/FeatureHashing/Adaptive%20Low-Precision%20Training%20for%20Embeddings%20in%20Click-Through%20Rate%20Prediction.pdf)
 - [Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems](Industry/FeatureHashing/Compositional%20Embeddings%20Using%20Complementary%20Partitions%20for%20Memory-Efficient%20Recommendation%20Systems.pdf)
 - [Feature Hashing for Large Scale Multitask Learning](Industry/FeatureHashing/Feature%20Hashing%20for%20Large%20Scale%20Multitask%20Learning.pdf)
 - [Getting Deep Recommenders Fit - Bloom Embeddings for Sparse Binary Input Output Networks](Industry/FeatureHashing/Getting%20Deep%20Recommenders%20Fit%20-%20Bloom%20Embeddings%20for%20Sparse%20Binary%20Input%20Output%20Networks.pdf)
@@ -407,6 +408,7 @@
 - [[2020][PCGrad] Gradient Surgery for Multi-Task Learning](Multi-Task/%5B2020%5D%5BPCGrad%5D%20Gradient%20Surgery%20for%20Multi-Task%20Learning.pdf)
 - [[2021][Meituan][AITM] Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](Multi-Task/%5B2021%5D%5BMeituan%5D%5BAITM%5D%20Modeling%20the%20Sequential%20Dependence%20among%20Audience%20Multi-step%20Conversions%20with%20Multi-task%20Learning%20in%20Targeted%20Display%20Advertising.pdf)
 - [[2022][Alibaba][ESCM2] ESCM2 - Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](Multi-Task/%5B2022%5D%5BAlibaba%5D%5BESCM2%5D%20ESCM2%20-%20Entire%20Space%20Counterfactual%20Multi-Task%20Model%20for%20Post-Click%20Conversion%20Rate%20Estimation.pdf)
+- [AdaTask - A Task-aware Adaptive Learning Rate Approach to Multi-task Learning](Multi-Task/AdaTask%20-%20A%20Task-aware%20Adaptive%20Learning%20Rate%20Approach%20to%20Multi-task%20Learning.pdf)
 - [Can Small Heads Help Understanding and Improving Multi-Task Generalization](Multi-Task/Can%20Small%20Heads%20Help%20Understanding%20and%20Improving%20Multi-Task%20Generalization.pdf)
 - [Cross-stitch Networks for Multi-task Learning](Multi-Task/Cross-stitch%20Networks%20for%20Multi-task%20Learning.pdf)
 - [Dynamic Task Prioritization for Multitask Learning](Multi-Task/Dynamic%20Task%20Prioritization%20for%20Multitask%20Learning.pdf)
@@ -436,12 +438,13 @@
 - [[2020][JD][DADNN] DADNN - Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network](Multi-Scenario/%5B2020%5D%5BJD%5D%5BDADNN%5D%20DADNN%20-%20Multi-Scene%20CTR%20Prediction%20via%20Domain-Aware%20Deep%20Neural%20Network.pdf)
 - [[2021][Alibaba][STAR] One Model to Serve All - Star Topology Adaptive Recommenderfor Multi-Domain CTR Prediction](Multi-Scenario/%5B2021%5D%5BAlibaba%5D%5BSTAR%5D%20One%20Model%20to%20Serve%20All%20-%20Star%20Topology%20Adaptive%20Recommenderfor%20Multi-Domain%20CTR%20Prediction.pdf)
 - [[2021][Baidu] Multi-Task and Multi-Scene Unified Ranking Model for Online Advertising](Multi-Scenario/%5B2021%5D%5BBaidu%5D%20Multi-Task%20and%20Multi-Scene%20Unified%20Ranking%20Model%20for%20Online%20Advertising.pdf)
+- [[2022][AntGroup][AESM2] Automatic Expert Selection for Multi-Scenario and Multi-Task Search](Multi-Scenario/%5B2022%5D%5BAntGroup%5D%5BAESM2%5D%20Automatic%20Expert%20Selection%20for%20Multi-Scenario%20and%20Multi-Task%20Search.pdf)
 - [A Deep Framework for Cross-Domain and Cross-System Recommendations](Multi-Scenario/A%20Deep%20Framework%20for%20Cross-Domain%20and%20Cross-System%20Recommendations.pdf)
 - [APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction](Multi-Scenario/APG%20-%20Adaptive%20Parameter%20Generation%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
 - [AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction](Multi-Scenario/AdaSparse%20-%20Learning%20Adaptively%20Sparse%20Structures%20for%20Multi-Domain%20Click-Through%20Rate%20Prediction.pdf)
 - [A Survey on Cross-domain Recommendation - Taxonomies, Methods, and Future Directions](Multi-Scenario/A%20Survey%20on%20Cross-domain%20Recommendation%20-%20Taxonomies%2C%20Methods%2C%20and%20Future%20Directions.pdf)
 - [Adaptive Domain Interest Network for Multi-domain Recommendation](Multi-Scenario/Adaptive%20Domain%20Interest%20Network%20for%20Multi-domain%20Recommendation.pdf)
-- [Automatic Expert Selection for Multi-Scenario and Multi-Task Search](Multi-Scenario/Automatic%20Expert%20Selection%20for%20Multi-Scenario%20and%20Multi-Task%20Search.pdf)
+- [Cross-domain recommendation via user interest alignment](Multi-Scenario/Cross-domain%20recommendation%20via%20user%20interest%20alignment.pdf)
 - [Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks](Multi-Scenario/Cross%20Domain%20Recommendation%20via%20Bi-directional%20Transfer%20Graph%20Collaborative%20Filtering%20Networks.pdf)
 - [Cross-Domain Recommendation - An Embedding and Mapping Approach](Multi-Scenario/Cross-Domain%20Recommendation%20-%20An%20Embedding%20and%20Mapping%20Approach.pdf)
 - [CoNet - Collaborative Cross Networks for Cross-Domain Recommendation](Multi-Scenario/CoNet%20-%20Collaborative%20Cross%20Networks%20for%20Cross-Domain%20Recommendation.pdf)

Multi-Scenario/Cross-domain recommendation via user interest alignment.pdf

@@ -1,7 +1,7 @@
 
 # Summary of Papers Related to Recommendation System
 ## Introduce
-1. Up to 2023-02-05, **553** papers related to recommendation system have been collected and summarized in this repo, 
+1. Up to 2023-02-12, **556** papers related to recommendation system have been collected and summarized in this repo, 
 including: **Match**, **Pre-Rank**, **Rank**, **Re-Rank**, **Multi-Task**, **Multi-Scenario**, **Multi-Modal**, **Cold-Start**, **Calibration**, 
 **Debias**, **Diversity**, **Fairness**, **Feedback-Delay**, **Distillation**, **Contrastive Learning**, **Casual Inference**, 
 **Look-Alike**, **Learning-to-Rank**, **ReinForce Learning** and other fields, the repo will track the industry progress and update continuely.
@@ -204,6 +204,7 @@ I will remove it immediately after verification.
 - [[2022][Pinterest][PinnerFormer] PinnerFormer - Sequence Modeling for User Representation at Pinterest](Industry/Representation/%5B2022%5D%5BPinterest%5D%5BPinnerFormer%5D%20PinnerFormer%20-%20Sequence%20Modeling%20for%20User%20Representation%20at%20Pinterest.pdf)
 #### FeatureHashing
 - [[2021][Google][DHE] Learning to Embed Categorical Features without Embedding Tables for Recommendation](Industry/FeatureHashing/%5B2021%5D%5BGoogle%5D%5BDHE%5D%20Learning%20to%20Embed%20Categorical%20Features%20without%20Embedding%20Tables%20for%20Recommendation.pdf)
+- [Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction](Industry/FeatureHashing/Adaptive%20Low-Precision%20Training%20for%20Embeddings%20in%20Click-Through%20Rate%20Prediction.pdf)
 - [Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems](Industry/FeatureHashing/Compositional%20Embeddings%20Using%20Complementary%20Partitions%20for%20Memory-Efficient%20Recommendation%20Systems.pdf)
 - [Feature Hashing for Large Scale Multitask Learning](Industry/FeatureHashing/Feature%20Hashing%20for%20Large%20Scale%20Multitask%20Learning.pdf)
 - [Getting Deep Recommenders Fit - Bloom Embeddings for Sparse Binary Input Output Networks](Industry/FeatureHashing/Getting%20Deep%20Recommenders%20Fit%20-%20Bloom%20Embeddings%20for%20Sparse%20Binary%20Input%20Output%20Networks.pdf)
@@ -414,6 +415,7 @@ I will remove it immediately after verification.
 - [[2020][PCGrad] Gradient Surgery for Multi-Task Learning](Multi-Task/%5B2020%5D%5BPCGrad%5D%20Gradient%20Surgery%20for%20Multi-Task%20Learning.pdf)
 - [[2021][Meituan][AITM] Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](Multi-Task/%5B2021%5D%5BMeituan%5D%5BAITM%5D%20Modeling%20the%20Sequential%20Dependence%20among%20Audience%20Multi-step%20Conversions%20with%20Multi-task%20Learning%20in%20Targeted%20Display%20Advertising.pdf)
 - [[2022][Alibaba][ESCM2] ESCM2 - Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](Multi-Task/%5B2022%5D%5BAlibaba%5D%5BESCM2%5D%20ESCM2%20-%20Entire%20Space%20Counterfactual%20Multi-Task%20Model%20for%20Post-Click%20Conversion%20Rate%20Estimation.pdf)
+- [AdaTask - A Task-aware Adaptive Learning Rate Approach to Multi-task Learning](Multi-Task/AdaTask%20-%20A%20Task-aware%20Adaptive%20Learning%20Rate%20Approach%20to%20Multi-task%20Learning.pdf)
 - [Can Small Heads Help Understanding and Improving Multi-Task Generalization](Multi-Task/Can%20Small%20Heads%20Help%20Understanding%20and%20Improving%20Multi-Task%20Generalization.pdf)
 - [Cross-stitch Networks for Multi-task Learning](Multi-Task/Cross-stitch%20Networks%20for%20Multi-task%20Learning.pdf)
 - [Dynamic Task Prioritization for Multitask Learning](Multi-Task/Dynamic%20Task%20Prioritization%20for%20Multitask%20Learning.pdf)
@@ -443,12 +445,13 @@ I will remove it immediately after verification.
 - [[2020][JD][DADNN] DADNN - Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network](Multi-Scenario/%5B2020%5D%5BJD%5D%5BDADNN%5D%20DADNN%20-%20Multi-Scene%20CTR%20Prediction%20via%20Domain-Aware%20Deep%20Neural%20Network.pdf)
 - [[2021][Alibaba][STAR] One Model to Serve All - Star Topology Adaptive Recommenderfor Multi-Domain CTR Prediction](Multi-Scenario/%5B2021%5D%5BAlibaba%5D%5BSTAR%5D%20One%20Model%20to%20Serve%20All%20-%20Star%20Topology%20Adaptive%20Recommenderfor%20Multi-Domain%20CTR%20Prediction.pdf)
 - [[2021][Baidu] Multi-Task and Multi-Scene Unified Ranking Model for Online Advertising](Multi-Scenario/%5B2021%5D%5BBaidu%5D%20Multi-Task%20and%20Multi-Scene%20Unified%20Ranking%20Model%20for%20Online%20Advertising.pdf)
+- [[2022][AntGroup][AESM2] Automatic Expert Selection for Multi-Scenario and Multi-Task Search](Multi-Scenario/%5B2022%5D%5BAntGroup%5D%5BAESM2%5D%20Automatic%20Expert%20Selection%20for%20Multi-Scenario%20and%20Multi-Task%20Search.pdf)
 - [A Deep Framework for Cross-Domain and Cross-System Recommendations](Multi-Scenario/A%20Deep%20Framework%20for%20Cross-Domain%20and%20Cross-System%20Recommendations.pdf)
 - [APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction](Multi-Scenario/APG%20-%20Adaptive%20Parameter%20Generation%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
 - [AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction](Multi-Scenario/AdaSparse%20-%20Learning%20Adaptively%20Sparse%20Structures%20for%20Multi-Domain%20Click-Through%20Rate%20Prediction.pdf)
 - [A Survey on Cross-domain Recommendation - Taxonomies, Methods, and Future Directions](Multi-Scenario/A%20Survey%20on%20Cross-domain%20Recommendation%20-%20Taxonomies%2C%20Methods%2C%20and%20Future%20Directions.pdf)
 - [Adaptive Domain Interest Network for Multi-domain Recommendation](Multi-Scenario/Adaptive%20Domain%20Interest%20Network%20for%20Multi-domain%20Recommendation.pdf)
-- [Automatic Expert Selection for Multi-Scenario and Multi-Task Search](Multi-Scenario/Automatic%20Expert%20Selection%20for%20Multi-Scenario%20and%20Multi-Task%20Search.pdf)
+- [Cross-domain recommendation via user interest alignment](Multi-Scenario/Cross-domain%20recommendation%20via%20user%20interest%20alignment.pdf)
 - [Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks](Multi-Scenario/Cross%20Domain%20Recommendation%20via%20Bi-directional%20Transfer%20Graph%20Collaborative%20Filtering%20Networks.pdf)
 - [Cross-Domain Recommendation - An Embedding and Mapping Approach](Multi-Scenario/Cross-Domain%20Recommendation%20-%20An%20Embedding%20and%20Mapping%20Approach.pdf)
 - [CoNet - Collaborative Cross Networks for Cross-Domain Recommendation](Multi-Scenario/CoNet%20-%20Collaborative%20Cross%20Networks%20for%20Cross-Domain%20Recommendation.pdf)