Diverse-RecSys

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Collection of diverse recommendation papers

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* 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.

# Awesome Diverse-RecSys Papers
Inspired by [Jihoo-Kim's repository](https://github.com/jihoo-kim/awesome-RecSys), 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

## Diverse Recommendation Papers
* [Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning](https://arxiv.org/pdf/2110.15097.pdf) (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](https://dl.acm.org/doi/abs/10.1145/3459637.3481940) (CIKM'21, Wenzhuo Yang(Salesforce Research) et al.)
* [Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network](https://arxiv.org/pdf/2102.03787.pdf) (IEEE T-Big data(Early Access), Ruobing Xie(Tencent) et al.)
* [I Want to Break Free! Recommending Friends from Outside the Echo Chamber](https://dl.acm.org/doi/10.1145/3460231.3474270) (Recsys'21, Antonela Tommasel et al.
)
* [Dynamic Graph Construction for Improving Diversity of Recommendation](https://dl.acm.org/doi/10.1145/3460231.3478845) (Recsys'21, Rui Ye et al.)
* [Towards Unified Metrics for Accuracy and Diversity for Recommender Systems](https://research.google/pubs/pub50607/) (Recsys'21, Javier Parapar et al.)
* [Popularity Bias in Dynamic Recommendation](http://people.tamu.edu/~zhuziwei/pubs/Ziwei_KDD_2021.pdf) (KDD'21, Ziwei Zhu et al.)
* [Auditing for Diversity using Representative Examples](https://arxiv.org/pdf/2107.07393.pdf) (KDD'21, Vijay Keswani et al.)
* [PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design](https://arxiv.org/pdf/2106.03620.pdf) (KDD'21, Amin Heyrani Nobari et al.)
* [Sliding Spectrum Decomposition for Diversified Recommendation](https://arxiv.org/pdf/2107.05204.pdf) (KDD'21, Yanhua Huang et al.)
* [Fairness among New Items in Cold Start Recommender Systems](https://dl.acm.org/doi/10.1145/3404835.3462948) (SIGIR'21, Ziwei Zhu et al.)
* [Modeling Intent Graph for Search Result Diversification](https://dl.acm.org/doi/10.1145/3404835.3462872) (SIGIR'21, Zhan Su et al.)
* [Graph Meta Network for Multi-Behavior Recommendation](https://dl.acm.org/doi/abs/10.1145/3404835.3462972) (SIGIR'21, Lianghao Xia et al.)
* [Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation](https://dl.acm.org/doi/10.1145/3404835.3462957) (SIGIR'21, Yile Liang et al.)
* [Accuracy-diversity trade-off in recommender systems via graph convolutions](https://www.sciencedirect.com/science/article/pii/S0306457320309511#!) (Information Processing & Management(Mar'21), Elvin Isufi(Delft Univ.) et al.)
* [Multi-Session Diversity to Improve User Satisfaction in Web Applications](https://dl.acm.org/doi/abs/10.1145/3442381.3450046) (WWW'21, Mohammadreza Esfandiari et al.)
* [DGCN: Diversified Recommendation with Graph Convolutional Networks](http://fi.ee.tsinghua.edu.cn/public/publications/b344fd48-92b0-11eb-96bc-0242ac120003.pdf) (WWW'21, Yu Zheng et al.)
* [Future-Aware Diverse Trends Framework for Recommendation](https://arxiv.org/pdf/2011.00422.pdf) (WWW'21, Yujie Lu et al.)
* [Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks](https://arxiv.org/pdf/2102.09635.pdf) (WWW'21, Bibek Paudel
 et al.)
* [A Hybrid Bandit Framework for Diversified Recommendation](https://arxiv.org/pdf/2012.13245.pdf) (AAAI'21, Qinxu Ding et al.)
* [Shifting Consumption towards Diverse Content on Music Streaming Platforms](https://dl.acm.org/doi/10.1145/3437963.3441775) (WSDM'21, Christian Hansen et al.)
* [Diverse User Preference Elicitation with Multi-Armed Bandits](https://dl.acm.org/doi/pdf/10.1145/3437963.3441786) (WSDM'21, Javier Parapar et al.)
* [Towards Long-term Fairness in Recommendation](https://arxiv.org/pdf/2101.03584.pdf) (WSDM'21, Yingqiang Ge et al.)
* [Diversified Interactive Recommendation with Implicit Feedback](https://aaai.org/ojs/index.php/AAAI/article/view/5931/5787) (AAAI'20, Yong Liu et al.)
* [Improving End-to-End Sequential Recommendations with Intent-aware Diversification](https://arxiv.org/pdf/1908.10171.pdf) (CIKM'20, Wanyu Chen et al.)
* [Controllable Multi-Interest Framework for Recommendation](https://arxiv.org/pdf/2005.09347.pdf) (KDD'20. Yukuo Cen et al.)
* [A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3394486.3403254) (KDD'20, Jianing Sun et al.)
* [Managing Diversity in Airbnb Search](https://arxiv.org/pdf/2004.02621.pdf) (KDD'20, Mustafa Abdool et al.)
* [DVGAN: A Minimax Game for Search Result Diversification Combining Explicit and Implicit Features](http://playbigdata.ruc.edu.cn/dou/publication/2020_sigir_divgan.pdf) (SIGIR'20, Qiong Wu et al.)
* [Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs](https://dl.acm.org/doi/abs/10.1145/3397271.3401213) (SIGIR'20, Lu Gan et al.)
* [Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity](https://arxiv.org/pdf/1912.00508.pdf) (Recsys'20, Chang Li et al.)
* [div2vec: Diversity-Emphasized Node Embedding](https://arxiv.org/ftp/arxiv/papers/2009/2009.09588.pdf) (RecSys'20, Jisu Jeong et al.)
* [MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search](http://proceedings.mlr.press/v108/han20b/han20b.pdf) (AISTATS'20, Insu Han et al.)
* [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/pdf/1904.08030.pdf) (CIKM'19, Chao Li et al.)
* [Sequential and Diverse Recommendation with Long Tail](https://www.ijcai.org/Proceedings/2019/0380.pdf) (IJCAI'19, Yejin Kim et al.)
* [PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation](https://www.ijcai.org/Proceedings/2019/0537.pdf) (IJCAI'19, Qiong Wu et al.)
* [Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation](https://arxiv.org/pdf/1903.07826.pdf) (19, Yong Liu et al.)
* [A Joint Optimization Approach for Personalized Recommendation Diversification](http://www.ruizhang.info/publications/PAKDD2018_Personalized%20Recommendation%20Diversification.pdf) (PAKDD'18, Xiaojie Wang et al.)
* [Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity](https://proceedings.neurips.cc/paper/2018/file/dbbf603ff0e99629dda5d75b6f75f966-Paper.pdf) (NeurIPS'18, Laming Chen et al.)
* [Practical Diversified Recommendations on YouTube with Determinantal Point Processes](https://jgillenw.com/cikm2018.pdf) (CIKM'18, Mark Wilhelm et al.)
* [Learning to Recommend Accurate and Diverse Items](http://wangshuaiqiang.net/publications/WWW17.pdf) (WWW'17, Peizhe Cheng et al.)
* [Post Processing Recommender Systems for Diversity](http://www.contrib.andrew.cmu.edu/~ravi/kdd17.pdf) (KDD'17, Arda Antikacioglu et al.)
* [Optimal Greedy Diversity for Recommendation](https://www.ijcai.org/Proceedings/15/Papers/248.pdf) (IJCAI'15, Azin Ashkan et al.)
* [Novel Recommendation Based on Personal Popularity Tendency](https://ieeexplore.ieee.org/document/6137255) (ICDM'11, Jinoh Oh et al.)

### Diversity Metric
* [Algorithmic Effects on the Diversity of Consumption on Spotify](http://www.cs.toronto.edu/~ashton/pubs/alg-effects-spotify-www2020.pdf) (WWW'20, Ashton Anderson et al.)
* [Preference based evaluation measures for novelty and diversity](http://184pc128.csie.ntnu.edu.tw/presentation/14-04-08/Preference%20Based%20Evaluation%20Measures%20for%20Novelty%20and%20Diversity.pdf) (SIGIR'13, Praveen Chandar et al.)
* [Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.8174&rep=rep1&type=pdf) (12, Gediminas Adomavicius et al.)
* [Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems](http://ir.ii.uam.es/pubs/recsys11-vargas.pdf) (RecSys'11, Saúl Vargas et al.)
* [Novelty and Diversity in Information Retrieval Evaluation](https://plg.uwaterloo.ca/~gvcormac/novelty.pdf) (SIGIR'08, Charles L. A. Clarke et al.)
* [Improving Recommendation Lists Through Topic Diversification](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.9683&rep=rep1&type=pdf) (WWW'05, Cai-Nicolas Ziegler et al.)

### Diverse Recommendation Survey
* [Recent Advances in Diversified Recommendation](https://arxiv.org/pdf/1905.06589.pdf) (19, Qiong Wu et al.)
* [Diversity in recommender systems – A survey](https://papers-gamma.link/static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf) (17, Matevž Kunaver et al.)
* [A Survey of Diversification Techniques in Recommendation Systems](https://www.researchgate.net/publication/311610832_A_survey_of_diversification_techniques_in_Recommendation_Systems) (16, Jayeeta Chakraborty et al.)

## Various mathods can applied to Diverse-RecSys\
* [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://arxiv.org/pdf/1912.02413.pdf) (CVPR'20, Boyan Zhou et al.)
* [Bayesian Graph Convolutional Neural Networks using Node Copying](https://arxiv.org/pdf/1911.04965.pdf) (ICML'19, Soumyasundar Pal et al.)
* [Bayesian graph convolutional neural networks for semi-supervised classification](https://arxiv.org/pdf/1811.11103.pdf) (AAAI'19, Yingxue Zhang et al)
* [Further Optimal Regret Bounds for Thompson Sampling](http://proceedings.mlr.press/v31/agrawal13a.pdf) (AISTATS'13, Shipra Agrawal et al.)
* [k-DPPs: Fixed-Size Determinantal Point Processes](https://icml.cc/2011/papers/611_icmlpaper.pdf) (ICML'11, Alex Kulesza et al.)

## Another RecSys Papers
* [Graph Neural Networks in Recommender Systems: A Survey](https://arxiv.org/pdf/2011.02260.pdf) (20, Shiwen Wu et al.)
* [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://arxiv.org/pdf/2002.02126.pdf) (SIGIR'20, Xiangnan He et al.)
* [Session-based Recommendation with Graph Neural Networks](https://arxiv.org/pdf/1811.00855.pdf) (AAAI'19, Shu Wu et al.)
* [Neural Graph Collaborative Filtering](https://arxiv.org/pdf/1905.08108.pdf) (SIGIR'19, Xiang Wang et al.)
* [Variational Autoencoders for Collaborative Filtering](https://arxiv.org/pdf/1802.05814.pdf) (WWW'18, Dawen Liang et al.)
* [Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) (WWW'17, Xiangnan He et al.)
* [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/pdf/1511.06939.pdf) (ICLR'16, Balazs Hidasi et al.)
* [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](https://alicezheng.org/papers/wsdm16-cdae.pdf) (WSDM'16, Yao Wu et al.)
* [BPR: Bayesian Personalized Ranking from Implicit Feedback](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) (UAI'09, Steffen Rendle et al.)
* [Matrix Factorization Techniques for Recommender Systems](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) (09, Yehuda Koren et al.)

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