update

Author: tangxyw

Match/M5 - Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top 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2024-10-14，本仓库收集汇总了推荐系统领域相关论文共**839**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2024-10-15，本仓库收集汇总了推荐系统领域相关论文共**840**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)。
@@ -429,6 +429,7 @@
 - [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](Match/Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data.pdf)
 - [Locker - Locally Constrained Self-Attentive Sequential Recommendation](Match/Locker%20-%20Locally%20Constrained%20Self-Attentive%20Sequential%20Recommendation.pdf)
 - [Multi-Objective Personalized Product Retrieval in Taobao Search](Match/Multi-Objective%20Personalized%20Product%20Retrieval%20in%20Taobao%20Search.pdf)
+- [M5 - Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation](Match/M5%20-%20Multi-Modal%20Multi-Interest%20Multi-Scenario%20Matching%20for%20Over-the-Top%20Recommendation.pdf)
 - [Modeling Dynamic Missingness of Implicit Feedback for Recommendation](Match/Modeling%20Dynamic%20Missingness%20of%20Implicit%20Feedback%20for%20Recommendation.pdf)
 - [MV-HAN - A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation](Match/MV-HAN%20-%20A%20Hybrid%20Attentive%20Networks%20based%20Multi-View%20Learning%20Model%20for%20Large-scale%20Contents%20Recommendation.pdf)
 - [Neighborhood-based Hard Negative Mining for Sequential Recommendation](Match/Neighborhood-based%20Hard%20Negative%20Mining%20for%20Sequential%20Recommendation.pdf)

README.md

@@ -1,7 +1,7 @@
 
 # Summary of Papers Related to Recommendation System
 ## Introduce
-1. Up to 2024-10-14, **839** papers related to recommendation system have been collected and summarized in this repo, 
+1. Up to 2024-10-15, **840** 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.
@@ -436,6 +436,7 @@ I will remove it immediately after verification.
 - [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](Match/Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data.pdf)
 - [Locker - Locally Constrained Self-Attentive Sequential Recommendation](Match/Locker%20-%20Locally%20Constrained%20Self-Attentive%20Sequential%20Recommendation.pdf)
 - [Multi-Objective Personalized Product Retrieval in Taobao Search](Match/Multi-Objective%20Personalized%20Product%20Retrieval%20in%20Taobao%20Search.pdf)
+- [M5 - Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation](Match/M5%20-%20Multi-Modal%20Multi-Interest%20Multi-Scenario%20Matching%20for%20Over-the-Top%20Recommendation.pdf)
 - [Modeling Dynamic Missingness of Implicit Feedback for Recommendation](Match/Modeling%20Dynamic%20Missingness%20of%20Implicit%20Feedback%20for%20Recommendation.pdf)
 - [MV-HAN - A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation](Match/MV-HAN%20-%20A%20Hybrid%20Attentive%20Networks%20based%20Multi-View%20Learning%20Model%20for%20Large-scale%20Contents%20Recommendation.pdf)
 - [Neighborhood-based Hard Negative Mining for Sequential Recommendation](Match/Neighborhood-based%20Hard%20Negative%20Mining%20for%20Sequential%20Recommendation.pdf)