update

Author: tangxyw

Industry/FinalMLP - An Enhanced Two-Stream MLP Model for CTR 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2023-04-07，本仓库收集汇总了推荐系统领域相关论文共**647**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2023-04-10，本仓库收集汇总了推荐系统领域相关论文共**649**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)。
@@ -108,7 +108,9 @@
 - [Fi-GNN - Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](Industry/Fi-GNN%20-%20Modeling%20Feature%20Interactions%20via%20Graph%20Neural%20Networks%20for%20CTR%20Prediction.pdf)
 - [FLEN - Leveraging Field for Scalable CTR Prediction](Industry/FLEN%20-%20Leveraging%20Field%20for%20Scalable%20CTR%20Prediction.pdf)
 - [FiBiNet++ - Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction](Industry/FiBiNet%2B%2B%20-%20Improving%20FiBiNet%20by%20Greatly%20Reducing%20Model%20Size%20for%20CTR%20Prediction.pdf)
+- [FinalMLP - An Enhanced Two-Stream MLP Model for CTR Prediction](Industry/FinalMLP%20-%20An%20Enhanced%20Two-Stream%20MLP%20Model%20for%20CTR%20Prediction.pdf)
 - [GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction](Industry/GateNet%20-%20Gating-Enhanced%20Deep%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
+- [General-Purpose User Embeddings based on Mobile App Usage](Industry/General-Purpose%20User%20Embeddings%20based%20on%20Mobile%20App%20Usage.pdf)
 - [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](Industry/Hierarchically%20Fusing%20Long%20and%20Short-Term%20User%20Interests%20for%20Click-Through%20Rate%20Prediction%20in%20Product%20Search.pdf)
 - [Hybrid Interest Modeling for Long-tailed Users](Industry/Hybrid%20Interest%20Modeling%20for%20Long-tailed%20Users.pdf)
 - [Hierarchical Gating Networks for Sequential Recommendation](Industry/Hierarchical%20Gating%20Networks%20for%20Sequential%20Recommendation.pdf)
@@ -481,6 +483,7 @@
 - [[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)
+- [[2023][Meituan][HiNet] HiNet - Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](Multi-Scenario/%5B2023%5D%5BMeituan%5D%5BHiNet%5D%20HiNet%20-%20Novel%20Multi-Scenario%20%26%20Multi-Task%20Learning%20with%20Hierarchical%20Information%20Extraction.pdf)
 - [[2023][Kuaishou][PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](Multi-Scenario/%5B2023%5D%5BKuaishou%5D%5BPEPNet%5D%20PEPNet%20-%20Parameter%20and%20Embedding%20Personalized%20Network%20for%20Infusing%20with%20Personalized%20Prior%20Information.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)
@@ -503,7 +506,6 @@
 - [DeepAPF - Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation](Multi-Scenario/DeepAPF%20-%20Deep%20Attentive%20Probabilistic%20Factorization%20for%20Multi-site%20Video%20Recommendation.pdf)
 - [DisenCDR - Learning Disentangled Representations for Cross-Domain Recommendation](Multi-Scenario/DisenCDR%20-%20Learning%20Disentangled%20Representations%20for%20Cross-Domain%20Recommendation.pdf)
 - [Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks](Multi-Scenario/Heterogeneous%20Graph%20Augmented%20Multi-Scenario%20Sharing%20Recommendation%20with%20Tree-Guided%20Expert%20Networks.pdf)
-- [HiNet - Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](Multi-Scenario/HiNet%20-%20Novel%20Multi-Scenario%20%26%20Multi-Task%20Learning%20with%20Hierarchical%20Information%20Extraction.pdf)
 - [Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space](Multi-Scenario/Improving%20Multi-Scenario%20Learning%20to%20Rank%20in%20E-commerce%20by%20Exploiting%20Task%20Relationships%20in%20the%20Label%20Space.pdf)
 - [KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](Multi-Scenario/KEEP%20-%20An%20Industrial%20Pre-Training%20Framework%20for%20Online%20Recommendation%20via%20Knowledge%20Extraction%20and%20Plugging.pdf)
 - [Leaving No One Behind - A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](Multi-Scenario/Leaving%20No%20One%20Behind%20-%20A%20Multi-Scenario%20Multi-Task%20Meta%20Learning%20Approach%20for%20Advertiser%20Modeling.pdf)

Industry/General-Purpose User Embeddings based on Mobile App Usage.pdf

@@ -1,7 +1,7 @@
 
 # Summary of Papers Related to Recommendation System
 ## Introduce
-1. Up to 2023-04-07, **647** papers related to recommendation system have been collected and summarized in this repo, 
+1. Up to 2023-04-10, **649** 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**, **Reinforcement Learning** and other fields, the repo will track the industry progress and update continuely.
@@ -115,7 +115,9 @@ I will remove it immediately after verification.
 - [Fi-GNN - Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](Industry/Fi-GNN%20-%20Modeling%20Feature%20Interactions%20via%20Graph%20Neural%20Networks%20for%20CTR%20Prediction.pdf)
 - [FLEN - Leveraging Field for Scalable CTR Prediction](Industry/FLEN%20-%20Leveraging%20Field%20for%20Scalable%20CTR%20Prediction.pdf)
 - [FiBiNet++ - Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction](Industry/FiBiNet%2B%2B%20-%20Improving%20FiBiNet%20by%20Greatly%20Reducing%20Model%20Size%20for%20CTR%20Prediction.pdf)
+- [FinalMLP - An Enhanced Two-Stream MLP Model for CTR Prediction](Industry/FinalMLP%20-%20An%20Enhanced%20Two-Stream%20MLP%20Model%20for%20CTR%20Prediction.pdf)
 - [GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction](Industry/GateNet%20-%20Gating-Enhanced%20Deep%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
+- [General-Purpose User Embeddings based on Mobile App Usage](Industry/General-Purpose%20User%20Embeddings%20based%20on%20Mobile%20App%20Usage.pdf)
 - [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](Industry/Hierarchically%20Fusing%20Long%20and%20Short-Term%20User%20Interests%20for%20Click-Through%20Rate%20Prediction%20in%20Product%20Search.pdf)
 - [Hybrid Interest Modeling for Long-tailed Users](Industry/Hybrid%20Interest%20Modeling%20for%20Long-tailed%20Users.pdf)
 - [Hierarchical Gating Networks for Sequential Recommendation](Industry/Hierarchical%20Gating%20Networks%20for%20Sequential%20Recommendation.pdf)
@@ -488,6 +490,7 @@ I will remove it immediately after verification.
 - [[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)
+- [[2023][Meituan][HiNet] HiNet - Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](Multi-Scenario/%5B2023%5D%5BMeituan%5D%5BHiNet%5D%20HiNet%20-%20Novel%20Multi-Scenario%20%26%20Multi-Task%20Learning%20with%20Hierarchical%20Information%20Extraction.pdf)
 - [[2023][Kuaishou][PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](Multi-Scenario/%5B2023%5D%5BKuaishou%5D%5BPEPNet%5D%20PEPNet%20-%20Parameter%20and%20Embedding%20Personalized%20Network%20for%20Infusing%20with%20Personalized%20Prior%20Information.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)
@@ -510,7 +513,6 @@ I will remove it immediately after verification.
 - [DeepAPF - Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation](Multi-Scenario/DeepAPF%20-%20Deep%20Attentive%20Probabilistic%20Factorization%20for%20Multi-site%20Video%20Recommendation.pdf)
 - [DisenCDR - Learning Disentangled Representations for Cross-Domain Recommendation](Multi-Scenario/DisenCDR%20-%20Learning%20Disentangled%20Representations%20for%20Cross-Domain%20Recommendation.pdf)
 - [Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks](Multi-Scenario/Heterogeneous%20Graph%20Augmented%20Multi-Scenario%20Sharing%20Recommendation%20with%20Tree-Guided%20Expert%20Networks.pdf)
-- [HiNet - Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](Multi-Scenario/HiNet%20-%20Novel%20Multi-Scenario%20%26%20Multi-Task%20Learning%20with%20Hierarchical%20Information%20Extraction.pdf)
 - [Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space](Multi-Scenario/Improving%20Multi-Scenario%20Learning%20to%20Rank%20in%20E-commerce%20by%20Exploiting%20Task%20Relationships%20in%20the%20Label%20Space.pdf)
 - [KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](Multi-Scenario/KEEP%20-%20An%20Industrial%20Pre-Training%20Framework%20for%20Online%20Recommendation%20via%20Knowledge%20Extraction%20and%20Plugging.pdf)
 - [Leaving No One Behind - A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](Multi-Scenario/Leaving%20No%20One%20Behind%20-%20A%20Multi-Scenario%20Multi-Task%20Meta%20Learning%20Approach%20for%20Advertiser%20Modeling.pdf)