• Currently I'm on other works instead of recommendation system, so I don't have enough time to catch up new papers related to diverse recommendation system and update this repository. Papers below are before 2022.

Inspired by Jihoo-Kim's repository, I share the collection of papers of diverse recommender system what I currently interested in.

There are many new diverse recommendation papers published, I will add them in this repository and notion as much as I can.

Besides, I add some papers that can help to understand various topics in recommendation, and various learning methods can be applied to recommendation systems.

• To be update: KDD'22, WWW'22, SIGIR'22

• Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning (WSDM'22, Dusan Stamenković(University of Novi Sad) et al.)
• On the Diversity and Explainability of Recommender Systems: A Practical Framework for Enterprise App Recommendation (CIKM'21, Wenzhuo Yang(Salesforce Research) et al.)
• Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network (IEEE T-Big data(Early Access), Ruobing Xie(Tencent) et al.)
• I Want to Break Free! Recommending Friends from Outside the Echo Chamber (Recsys'21, Antonela Tommasel et al. )
• Dynamic Graph Construction for Improving Diversity of Recommendation (Recsys'21, Rui Ye et al.)
• Towards Unified Metrics for Accuracy and Diversity for Recommender Systems (Recsys'21, Javier Parapar et al.)
• Popularity Bias in Dynamic Recommendation (KDD'21, Ziwei Zhu et al.)
• Auditing for Diversity using Representative Examples (KDD'21, Vijay Keswani et al.)
• PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design (KDD'21, Amin Heyrani Nobari et al.)
• Sliding Spectrum Decomposition for Diversified Recommendation (KDD'21, Yanhua Huang et al.)
• Fairness among New Items in Cold Start Recommender Systems (SIGIR'21, Ziwei Zhu et al.)
• Modeling Intent Graph for Search Result Diversification (SIGIR'21, Zhan Su et al.)
• Graph Meta Network for Multi-Behavior Recommendation (SIGIR'21, Lianghao Xia et al.)
• Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation (SIGIR'21, Yile Liang et al.)
• Accuracy-diversity trade-off in recommender systems via graph convolutions (Information Processing & Management(Mar'21), Elvin Isufi(Delft Univ.) et al.)
• Multi-Session Diversity to Improve User Satisfaction in Web Applications (WWW'21, Mohammadreza Esfandiari et al.)
• DGCN: Diversified Recommendation with Graph Convolutional Networks (WWW'21, Yu Zheng et al.)
• Future-Aware Diverse Trends Framework for Recommendation (WWW'21, Yujie Lu et al.)
• Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks (WWW'21, Bibek Paudel et al.)
• A Hybrid Bandit Framework for Diversified Recommendation (AAAI'21, Qinxu Ding et al.)
• Shifting Consumption towards Diverse Content on Music Streaming Platforms (WSDM'21, Christian Hansen et al.)
• Diverse User Preference Elicitation with Multi-Armed Bandits (WSDM'21, Javier Parapar et al.)
• Towards Long-term Fairness in Recommendation (WSDM'21, Yingqiang Ge et al.)
• Diversified Interactive Recommendation with Implicit Feedback (AAAI'20, Yong Liu et al.)
• Improving End-to-End Sequential Recommendations with Intent-aware Diversification (CIKM'20, Wanyu Chen et al.)
• Controllable Multi-Interest Framework for Recommendation (KDD'20. Yukuo Cen et al.)
• A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks (KDD'20, Jianing Sun et al.)
• Managing Diversity in Airbnb Search (KDD'20, Mustafa Abdool et al.)
• DVGAN: A Minimax Game for Search Result Diversification Combining Explicit and Implicit Features (SIGIR'20, Qiong Wu et al.)
• Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs (SIGIR'20, Lu Gan et al.)
• Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity (Recsys'20, Chang Li et al.)
• div2vec: Diversity-Emphasized Node Embedding (RecSys'20, Jisu Jeong et al.)
• MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search (AISTATS'20, Insu Han et al.)
• Multi-Interest Network with Dynamic Routing for Recommendation at Tmall (CIKM'19, Chao Li et al.)
• Sequential and Diverse Recommendation with Long Tail (IJCAI'19, Yejin Kim et al.)
• PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation (IJCAI'19, Qiong Wu et al.)
• Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation (19, Yong Liu et al.)
• A Joint Optimization Approach for Personalized Recommendation Diversification (PAKDD'18, Xiaojie Wang et al.)
• Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity (NeurIPS'18, Laming Chen et al.)
• Practical Diversified Recommendations on YouTube with Determinantal Point Processes (CIKM'18, Mark Wilhelm et al.)
• Learning to Recommend Accurate and Diverse Items (WWW'17, Peizhe Cheng et al.)
• Post Processing Recommender Systems for Diversity (KDD'17, Arda Antikacioglu et al.)
• Optimal Greedy Diversity for Recommendation (IJCAI'15, Azin Ashkan et al.)
• Novel Recommendation Based on Personal Popularity Tendency (ICDM'11, Jinoh Oh et al.)

• Algorithmic Effects on the Diversity of Consumption on Spotify (WWW'20, Ashton Anderson et al.)
• Preference based evaluation measures for novelty and diversity (SIGIR'13, Praveen Chandar et al.)
• Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques (12, Gediminas Adomavicius et al.)
• Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems (RecSys'11, Saúl Vargas et al.)
• Novelty and Diversity in Information Retrieval Evaluation (SIGIR'08, Charles L. A. Clarke et al.)
• Improving Recommendation Lists Through Topic Diversification (WWW'05, Cai-Nicolas Ziegler et al.)

• Recent Advances in Diversified Recommendation (19, Qiong Wu et al.)
• Diversity in recommender systems – A survey (17, Matevž Kunaver et al.)
• A Survey of Diversification Techniques in Recommendation Systems (16, Jayeeta Chakraborty et al.)

• BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition (CVPR'20, Boyan Zhou et al.)
• Bayesian Graph Convolutional Neural Networks using Node Copying (ICML'19, Soumyasundar Pal et al.)
• Bayesian graph convolutional neural networks for semi-supervised classification (AAAI'19, Yingxue Zhang et al)
• Further Optimal Regret Bounds for Thompson Sampling (AISTATS'13, Shipra Agrawal et al.)
• k-DPPs: Fixed-Size Determinantal Point Processes (ICML'11, Alex Kulesza et al.)

• Graph Neural Networks in Recommender Systems: A Survey (20, Shiwen Wu et al.)
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (SIGIR'20, Xiangnan He et al.)
• Session-based Recommendation with Graph Neural Networks (AAAI'19, Shu Wu et al.)
• Neural Graph Collaborative Filtering (SIGIR'19, Xiang Wang et al.)
• Variational Autoencoders for Collaborative Filtering (WWW'18, Dawen Liang et al.)
• Neural Collaborative Filtering (WWW'17, Xiangnan He et al.)
• Session-based Recommendations with Recurrent Neural Networks (ICLR'16, Balazs Hidasi et al.)
• Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (WSDM'16, Yao Wu et al.)
• BPR: Bayesian Personalized Ranking from Implicit Feedback (UAI'09, Steffen Rendle et al.)
• Matrix Factorization Techniques for Recommender Systems (09, Yehuda Koren et al.)