推薦システム論文リンク集

個人的に参照するためにまとめているリンク集です。

主に推薦システム関係のリストになります。

推薦システム一般

こちらのリストは、私が調べ物をしたときに参考にした論文をまとめています。

• [No. 0001] Matrix Factorization Techniques for Recommender Systems Recommender Systems
• [No. 0002] Deep Neural Networks for YouTube Recommendations
• [No. 0003] Self-Supervised Reinforcement Learning for Recommender Systems
• [No. 0004] Top-K Off-Policy Correction for a REINFORCE Recommender System
• [No. 0005] Improved Recurrent Neural Networks for Session-based Recommendations
• [No. 0006] Latent Cross: Making Use of Context in Recurrent Recommender Systems
• [No. 0007] Sequential User-based Recurrent Neural Network Recommendations
• [No. 0008] A Survey on Session-based Recommender Systems
• [No. 0009] A Multimedia Recommender System
• [No. 0010] Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
• [No. 0011] PyRecGym: a reinforcement learning gym for recommender systems
• [No. 0012] Usage-based web recommendations: a reinforcement learning approach
• [No. 0013] Deep reinforcement learning for page-wise recommendations
• [No. 0014] Interactive Recommendation with User-Specific Deep Reinforcement Learning
• [No. 0015] End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding
• [No. 0016] (Deep Reinforment) Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
• [No. 0017] Deep Learning Based Recommender System: A Survey and New Perspectives
• [No. 0020] Recommending what video to watch next: a multitask ranking system
• [No. 0021] Modeling Task Relationships in Multi-task Learning with Multi-gae Mixture-of-Experts
• [No. 0022] (tencent) TencentRec: Real-time Stream Recommendation in Practice
• [No. 0023] (tencent) Neural Rating Regression with Abstractive Tips Generation for Recommendation
• [No. 0024] (spotify) The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
• [No. 0025] (spotify) Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
• [No. 0026] (spotify contextual bandits) Carousel Personalization in Music Streaming Apps with Contextual Bandits
• [No. 0027] (spotify music re-ranking) Contextual Personalized Re-Ranking of Music Recommendations through Audio Features
• [No. 0028] (spotify bandit BaRT (Bandits for Recommendations as Treatments)) Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits
• [No. 0029] (Microsoft Recommenders Tool) Microsoft Recommenders
• [No. 0030] (Google transformer) Big Bird: Transformers for Longer Sequences
• [No. 0031] (transformer) Transformers are RNNs:Fast Autoregressive Transformers with Linear Attentio
• [No. 0032] (RecBole) RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
• [No. 0033] (deep learning) Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
• [No. 0034] (item2vec) ITEM2VEC: NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING
• [No. 0035] (Alibaba) Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
• [No. 0036] DeepWalk: Online Learning of Social Representations
• [No. 0037] (deep walk, random walk) Random-walk computation of similarities between nodes of a graph, with application to collaborative recommendation
• [No. 0038] (node2vec) node2vec: Scalable Feature Learning for Networks
• [No. 0039] (knowledge graph) Knowledge Graph Convolutional Networks for Recommender Systems
• [No. 0040] (light fm) Metadata Embeddings for User and Item Cold-start Recommendations
• [No. 0041] (MLP-Mixer) MLP-Mixer: An all-MLP Architecture for Vision

重要な論文リスト

こちらのリストは、

• https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising
• https://github.com/guyulongcs/Awesome-Deep-Reinforcement-Learning-Papers-for-Search-Recommendation-Advertising

に記載されているリストを私が読んだ順番にメモしています。

• [No. 2001] 2020 (Airbnb) (KDD) Improving Deep Learning For Airbnb Search
• [No. 2002] 2020 (Airbnb) (KDD) Managing Diversity in Airbnb Search
• [No. 2003] 2020 (Alibaba) (Arxiv) [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
• [No. 2004] 2020 (Alibaba) (ICML) [OTM] Learning Optimal Tree Models under Beam Search
• [No. 2005] 2020 (Alibaba) (KDD) Privileged Features Distillation at Taobao Recommendations
• [No. 2006] 2020 (Alibaba) (KDD) [ComiRec] Controllable Multi-Interest Framework for Recommendation
• [No. 2007] 2020 (Alibaba) (SIGIR) [ATBRG] ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
• [No. 2008] 2020 (Alibaba) (SIGIR) [DHAN] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
• [No. 2009] 2020 (Alibaba) (SIGIR) [ESM2] Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
• [No. 2010] 2020 (Alibaba) (WWW) [MARN] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
• [No. 2011] 2018 (Alibaba) (KDD) [DIN] Deep Interest Network for Click-Through Rate Prediction
• [No. 2012] 2019 (Alibaba) (AAAI) [DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction
• [No. 2013] 2020 (Alibaba) (CIKM) [MiNet] Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
• [No. 2014] 2020 (Baidu) (CIKM) [DeepChain] Whole-Chain Recommendations
• [No. 2015] 2020 (Baidu) (KDD) [CAN] Combo-Attention Network for Baidu Video Advertising

量子情報 + 推薦システム

こちらは興味がある量子情報(量子プログラミング)との組み合わせです。

• [No. 4001] Quantum Recommendation Systems
• [No. 4002] Recommender systems inspired by the structure of quantum theor
• [No. 4003] A quantum-inspired classical algorithm for recommendation systems
• [No. 4004] How Quantum Theory Is Developing the Field of Information Retrieval
• [No. 4005] A quantum-inspired classical algorithm for recommendation systems
• [No. 4006] Quantum-inspired classical algorithms for principal component analysis and supervised clustering
• [No. 4007] Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension
• [No. 4008] Quantum Algorithms for Linear Algebra and Machine Learning.
• [No. 4009] Quantum Graph Neural Networks
• [No. 4010] Graph Convolutional Neural Networks based on Quantum Vertex Saliency
• [No. 4011] Experimental Implementation of Quantum Walks on IBM Quantum Computers

数値計算、線型代数

• [No. 6001] Quantum Recommendation Systems
• [No. 6002] Quantum probability-inspired graph neural network for document representation and classification
• [No. 6003] A Survey of Quantum Theory Inspired Approaches to Information Retrival
• [No. 6004] A Quantum Interference Inspired Neural Matching Model for Ad-hoc Retrieval

CIKM 2021

• [No. 7001] UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
• [No. 7002] How Powerful is Graph Convolution for Recommendation?
• [No. 7003] Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
• [No. 7004] Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation
• [No. 7005] Self-Supervised Graph Co-Training for Session-based Recommendation
• [No. 7006] Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation
• [No. 7007] WG4Rec: Modeling Textual Content with Word Graph for News Recommendation
• [No. 7008] Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
• [No. 7009] Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation

グラフ、量子情報、推薦システム

• [No. 7101] LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• [No. 7102] Neural Graph Collaborative Filtering
• [No. 7103] Quantum-based subgraph convolutional neural networks
• [No. 7104] A Quantum Spatial Graph Convolutional Network for Text Classification
• [No. 7105] Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning
• [No. 7106] Experimental Implementation of Quantum Walks on IBM Quantum Computers
• [No. 7107] Hybrid Quantum-Classical Graph Convolutional Network
• [No. 7108] Quantum Graph Convolutional Neural Networks
• [No. 7110] Quantum Convolutional Neural Networks
• [No. 7111] SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS