update

Author: tangxyw

Match/AutoRec - Autoencoders Meet Collaborative Filtering.pdf

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 # 推荐系统相关论文汇总
 ([English Version is Here](/README_EN.md))
 ## 介绍
-1. 截至2022-11-11，本仓库收集汇总了推荐系统领域相关论文共**450**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2022-11-15，本仓库收集汇总了推荐系统领域相关论文共**461**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)。
@@ -222,24 +222,34 @@
 - [[2021][Alibaba][MGDSPR] Embedding-based Product Retrieval in Taobao Search](Match/%5B2021%5D%5BAlibaba%5D%5BMGDSPR%5D%20Embedding-based%20Product%20Retrieval%20in%20Taobao%20Search.pdf)
 - [Attentive Collaborative Filtering - Multimedia Recommendation with Item- and Component-Level A‚ention](Match/Attentive%20Collaborative%20Filtering%20-%20Multimedia%20Recommendation%20with%20Item-%20and%20Component-Level%20A%C2%82ention.pdf)
 - [A User-Centered Concept Mining System for Query and Document Understanding at Tencent](Match/A%20User-Centered%20Concept%20Mining%20System%20for%20Query%20and%20Document%20Understanding%20at%20Tencent.pdf)
+- [AutoRec - Autoencoders Meet Collaborative Filtering](Match/AutoRec%20-%20Autoencoders%20Meet%20Collaborative%20Filtering.pdf)
 - [A Dual Augmented Two-tower Model for Online Large-scale Recommendation](Match/A%20Dual%20Augmented%20Two-tower%20Model%20for%20Online%20Large-scale%20Recommendation.pdf)
 - [CROLoss - Towards a Customizable Loss for Retrieval Models in Recommender Systems](Match/CROLoss%20-%20Towards%20a%20Customizable%20Loss%20for%20Retrieval%20Models%20in%20Recommender%20Systems.pdf)
 - [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](Match/Collaborative%20Denoising%20Auto-Encoders%20for%20Top-N%20Recommender%20Systems.pdf)
 - [Cross-Batch Negative Sampling for Training Two-Tower Recommenders](Match/Cross-Batch%20Negative%20Sampling%20for%20Training%20Two-Tower%20Recommenders.pdf)
+- [Collaborative Deep Learning for Recommender Systems](Match/Collaborative%20Deep%20Learning%20for%20Recommender%20Systems.pdf)
 - [Deep Matrix Factorization Models for Recommender Systems](Match/Deep%20Matrix%20Factorization%20Models%20for%20Recommender%20Systems.pdf)
 - [Disentangled Self-Supervision in Sequential Recommenders](Match/Disentangled%20Self-Supervision%20in%20Sequential%20Recommenders.pdf)
+- [Deep Collaborative Filtering via Marginalized Denoising Auto-encoder](Match/Deep%20Collaborative%20Filtering%20via%20Marginalized%20Denoising%20Auto-encoder.pdf)
 - [Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations](Match/Deep%20Retrieval%20-%20Learning%20A%20Retrievable%20Structure%20for%20Large-Scale%20Recommendations.pdf)
 - [Efficient Training on Very Large Corpora via Gramian Estimation](Match/Efficient%20Training%20on%20Very%20Large%20Corpora%20via%20Gramian%20Estimation.pdf)
 - [Extreme Multi-label Learning for Semantic Matching in Product Search](Match/Extreme%20Multi-label%20Learning%20for%20Semantic%20Matching%20in%20Product%20Search.pdf)
 - [Factorization Meets the Neighborhood - a Multifaceted Collaborative Filtering Model](Match/Factorization%20Meets%20the%20Neighborhood%20-%20a%20Multifaceted%20Collaborative%20Filtering%20Model.pdf)
+- [Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems](Match/Hierarchical%20Temporal%20Convolutional%20Networks%20for%20Dynamic%20Recommender%20Systems.pdf)
 - [Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching](Match/Heterogeneous%20Graph%20Neural%20Networks%20for%20Large-Scale%20Bid%20Keyword%20Matching.pdf)
 - [Itinerary-aware Personalized Deep Matching at Fliggy](Match/Itinerary-aware%20Personalized%20Deep%20Matching%20at%20Fliggy.pdf)
+- [ItemSage - Learning Product Embeddings for Shopping Recommendations at Pinterest](Match/ItemSage%20-%20Learning%20Product%20Embeddings%20for%20Shopping%20Recommendations%20at%20Pinterest.pdf)
+- [Inferring Networks of Substitutable and Complementary Products](Match/Inferring%20Networks%20of%20Substitutable%20and%20Complementary%20Products.pdf)
 - [Joint Optimization of Tree-based Index and Deep Model for Recommender Systems](Match/Joint%20Optimization%20of%20Tree-based%20Index%20and%20Deep%20Model%20for%20Recommender%20Systems.pdf)
+- [Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking](Match/Latent%20Relational%20Metric%20Learning%20via%20Memory-based%20Attention%20for%20Collaborative%20Ranking.pdf)
 - [Learning Tree-based Deep Model for Recommender Systems](Match/Learning%20Tree-based%20Deep%20Model%20for%20Recommender%20Systems.pdf)
 - [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](Match/Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data.pdf)
 - [NAIS - Neural Attentive Item Similarity Model for Recommendation](Match/NAIS%20-%20Neural%20Attentive%20Item%20Similarity%20Model%20for%20Recommendation.pdf)
 - [Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates](Match/Octopus%20-%20Comprehensive%20and%20Elastic%20User%20Representation%20for%20the%20Generation%20of%20Recommendation%20Candidates.pdf)
+- [Outer Product-based Neural Collaborative Filtering](Match/Outer%20Product-based%20Neural%20Collaborative%20Filtering.pdf)
+- [PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest](Match/PinnerSage%20-%20Multi-Modal%20User%20Embedding%20Framework%20for%20Recommendations%20at%20Pinterest.pdf)
 - [Path-based Deep Network for Candidate Item Matching in Recommenders](Match/Path-based%20Deep%20Network%20for%20Candidate%20Item%20Matching%20in%20Recommenders.pdf)
+- [Sequential Recommendation via Stochastic Self-Attention](Match/Sequential%20Recommendation%20via%20Stochastic%20Self-Attention.pdf)
 - [Sparse-Interest Network for Sequential Recommendation](Match/Sparse-Interest%20Network%20for%20Sequential%20Recommendation.pdf)
 - [Self-Attentive Sequential Recommendation](Match/Self-Attentive%20Sequential%20Recommendation.pdf)
 - [Towards Personalized and Semantic Retrieval - An End-to-End Solution for E-commerce Search via Embedding Learning](Match/Towards%20Personalized%20and%20Semantic%20Retrieval%20-%20An%20End-to-End%20Solution%20for%20E-commerce%20Search%20via%20Embedding%20Learning.pdf)
@@ -278,6 +288,7 @@
 - [[2020][LightGCN] LightGCN - Simplifying and Powering Graph Convolution Network for Recommendation](Match/GNN/%5B2020%5D%5BLightGCN%5D%20LightGCN%20-%20Simplifying%20and%20Powering%20Graph%20Convolution%20Network%20for%20Recommendation.pdf)
 - [ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation](Match/GNN/ATBRG%20-%20Adaptive%20Target-Behavior%20Relational%20Graph%20Network%20for%20Effective%20Recommendation.pdf)
 - [Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View](Match/GNN/Attentional%20Graph%20Convolutional%20Networks%20for%20Knowledge%20Concept%20Recommendation%20in%20MOOCs%20in%20a%20Heterogeneous%20View.pdf)
+- [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](Match/GNN/Continuous-Time%20Sequential%20Recommendation%20with%20Temporal%20Graph%20Collaborative%20Transformer.pdf)
 - [Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems](Match/GNN/Debiasing%20Neighbor%20Aggregation%20for%20Graph%20Neural%20Network%20in%20Recommender%20Systems.pdf)
 - [DC-GNN - Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval](Match/GNN/DC-GNN%20-%20Decoupled%20Graph%20Neural%20Networks%20for%20Improving%20and%20Accelerating%20Large-Scale%20E-commerce%20Retrieval.pdf)
 - [Disentangled Graph Collaborative Filtering](Match/GNN/Disentangled%20Graph%20Collaborative%20Filtering.pdf)
@@ -292,11 +303,11 @@
 - [Graph Neural Networks for Social Recommendation](Match/GNN/Graph%20Neural%20Networks%20for%20Social%20Recommendation.pdf)
 - [GraphSAIL - Graph Structure Aware Incremental Learning for Recommender Systems](Match/GNN/GraphSAIL%20-%20Graph%20Structure%20Aware%20Incremental%20Learning%20for%20Recommender%20Systems.pdf)
 - [IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation](Match/GNN/IntentGC%20-%20a%20Scalable%20Graph%20Convolution%20Framework%20Fusing%20Heterogeneous%20Information%20for%20Recommendation.pdf)
+- [MultiSage - Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks](Match/GNN/MultiSage%20-%20Empowering%20GCN%20with%20Contextualized%20Multi-Embeddings%20on%20Web-Scale%20Multipartite%20Networks.pdf)
 - [MMGCN - Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video](Match/GNN/MMGCN%20-%20Multi-modal%20Graph%20Convolution%20Network%20for%20Personalized%20Recommendation%20of%20Micro-video.pdf)
 - [Network Embedding as Matrix Factorization - Unifying DeepWalk, LINE, PTE, and node2vec](Match/GNN/Network%20Embedding%20as%20Matrix%20Factorization%20-%20Unifying%20DeepWalk%2C%20LINE%2C%20PTE%2C%20and%20node2vec.pdf)
 - [Neighbor Interaction Aware Graph Convolution Networks for Recommendation](Match/GNN/Neighbor%20Interaction%20Aware%20Graph%20Convolution%20Networks%20for%20Recommendation.pdf)
 - [Package Recommendation with Intra- and Inter-Package Attention Networks](Match/GNN/Package%20Recommendation%20with%20Intra-%20and%20Inter-Package%20Attention%20Networks.pdf)
-- [PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest](Match/GNN/PinnerSage%20-%20Multi-Modal%20User%20Embedding%20Framework%20for%20Recommendations%20at%20Pinterest.pdf)
 - [ProNE - Fast and Scalable Network Representation Learning](Match/GNN/ProNE%20-%20Fast%20and%20Scalable%20Network%20Representation%20Learning.pdf)
 - [Representation Learning for Attributed Multiplex Heterogeneous Network](Match/GNN/Representation%20Learning%20for%20Attributed%20Multiplex%20Heterogeneous%20Network.pdf)
 - [Revisiting Item Promotion in GNN-based Collaborative Filtering - A Masked Targeted Topological Attack Perspective](Match/GNN/Revisiting%20Item%20Promotion%20in%20GNN-based%20Collaborative%20Filtering%20-%20A%20Masked%20Targeted%20Topological%20Attack%20Perspective.pdf)