update

Author: tangxyw

Multi-Scenario/DIIT - A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation.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2024-10-18，本仓库收集汇总了推荐系统领域相关论文共**845**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2024-10-20，本仓库收集汇总了推荐系统领域相关论文共**846**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)。
@@ -659,6 +659,7 @@
 - [DDTCDR - Deep Dual Transfer Cross Domain Recommendation](Multi-Scenario/DDTCDR%20-%20Deep%20Dual%20Transfer%20Cross%20Domain%20Recommendation.pdf)
 - [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)
+- [DIIT - A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation](Multi-Scenario/DIIT%20-%20A%20Domain-Invariant%20Information%20Transfer%20Method%20for%20Industrial%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)
 - [Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](Multi-Scenario/Hybrid%20Contrastive%20Constraints%20for%20Multi-Scenario%20Ad%20Ranking.pdf)
 - [HAMUR - Hyper Adapter for Multi-Domain Recommendation](Multi-Scenario/HAMUR%20-%20Hyper%20Adapter%20for%20Multi-Domain%20Recommendation.pdf)

README.md

@@ -1,7 +1,7 @@
 
 # Summary of Papers Related to Recommendation System
 ## Introduce
-1. Up to 2024-10-18, **845** papers related to recommendation system have been collected and summarized in this repo, 
+1. Up to 2024-10-20, **846** 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.
@@ -666,6 +666,7 @@ I will remove it immediately after verification.
 - [DDTCDR - Deep Dual Transfer Cross Domain Recommendation](Multi-Scenario/DDTCDR%20-%20Deep%20Dual%20Transfer%20Cross%20Domain%20Recommendation.pdf)
 - [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)
+- [DIIT - A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation](Multi-Scenario/DIIT%20-%20A%20Domain-Invariant%20Information%20Transfer%20Method%20for%20Industrial%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)
 - [Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](Multi-Scenario/Hybrid%20Contrastive%20Constraints%20for%20Multi-Scenario%20Ad%20Ranking.pdf)
 - [HAMUR - Hyper Adapter for Multi-Domain Recommendation](Multi-Scenario/HAMUR%20-%20Hyper%20Adapter%20for%20Multi-Domain%20Recommendation.pdf)