default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDRecSys 2020: Virtual Event, Brazil
- Rodrygo L. T. Santos, Leandro Balby Marinho, Elizabeth M. Daly, Li Chen, Kim Falk, Noam Koenigstein, Edleno Silva de Moura:
RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22-26, 2020. ACM 2020, ISBN 978-1-4503-7583-2
Invited Keynotes
- Filippo Menczer:
4 Reasons Why Social Media Make Us Vulnerable to Manipulation. 1 - Ricardo Baeza-Yates:
Bias in Search and Recommender Systems. 2 - Michelle X. Zhou:
"You Really Get Me": Conversational AI Agents That Can Truly Understand and Help Users. 3
Long Papers
- Yoshifumi Seki, Takanori Maehara:
A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets. 4-12 - Hanze Li, Scott Sanner, Kai Luo, Ga Wu:
A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems. 13-22 - Zhu Sun
, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, Cong Geng:
Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison. 23-32 - Chang Li, Haoyun Feng, Maarten de Rijke:
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity. 33-42 - Yin Zhang, Ziwei Zhu, Yun He, James Caverlee:
Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. 43-52 - Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas:
Contextual and Sequential User Embeddings for Large-Scale Music Recommendation. 53-62 - Xu He, Bo An, Yanghua Li, Haikai Chen, Qingyu Guo, Xin Li, Zhirong Wang:
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation. 63-72 - Tobias Schnabel, Paul N. Bennett:
Debiasing Item-to-Item Recommendations With Small Annotated Datasets. 73-81 - Guy Aridor, Duarte Gonçalves, Shan Sikdar:
Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems. 82-91 - Yuta Saito:
Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions. 92-100 - Mesut Kaya, Derek G. Bridge, Nava Tintarev:
Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance. 101-110 - Gustavo Penha, Rodrygo L. T. Santos:
Exploiting Performance Estimates for Augmenting Recommendation Ensembles. 111-119 - Liu Yang, Bo Liu, Leyu Lin, Feng Xia, Kai Chen, Qiang Yang:
Exploring Clustering of Bandits for Online Recommendation System. 120-129 - Jing Lin, Weike Pan, Zhong Ming:
FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation. 130-139 - Pigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Xiquan Cui, Edo Liberty, Khalifeh Al Jadda:
From the lab to production: A case study of session-based recommendations in the home-improvement domain. 140-149 - Huazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan, Hongning Wang:
Global and Local Differential Privacy for Collaborative Bandits. 150-159 - Samarth Aggarwal, Rohin Garg, Abhilasha Sancheti, Bhanu Prakash Reddy Guda, Iftikhar Ahamath Burhanuddin:
Goal-driven Command Recommendations for Analysts. 160-169 - James Neve, Ryan McConville:
ImRec: Learning Reciprocal Preferences Using Images. 170-179 - Jesús Omar Álvarez Márquez, Jürgen Ziegler:
In-Store Augmented Reality-Enabled Product Comparison and Recommendation. 180-189 - Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof:
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. 190-199 - Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, Xing Xie:
KRED: Knowledge-Aware Document Representation for News Recommendations. 200-209 - Xu He, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang:
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication. 210-219 - Darius Afchar, Romain Hennequin:
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction. 220-229 - Ahmed Rashed, Shayan Jawed, Lars Schmidt-Thieme, Andre Hintsches:
MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems. 230-239 - Steffen Rendle, Walid Krichene, Li Zhang, John R. Anderson:
Neural Collaborative Filtering vs. Matrix Factorization Revisited. 240-248 - Mawulolo K. Ameko, Miranda L. Beltzer, Lihua Cai, Mehdi Boukhechba, Bethany A. Teachman, Laura E. Barnes:
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation. 249-258 - Rocío Cañamares, Pablo Castells:
On Target Item Sampling in Offline Recommender System Evaluation. 259-268 - Hongyan Tang, Junning Liu, Ming Zhao, Xudong Gong:
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. 269-278 - Pan Li, Maofei Que, Zhichao Jiang, Yao Hu, Alexander Tuzhilin:
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction. 279-288 - Marialena Kyriakidi, Georgia Koutrika, Yannis E. Ioannidis:
Recommendations as Graph Explorations. 289-298 - Panagiotis Symeonidis, Andrea Janes, Dmitry Chaltsev, Philip Giuliani, Daniel Morandini, Andreas Unterhuber, Ludovik Coba, Markus Zanker:
Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de. 299-308 - Théo Moins, Daniel Aloise, Simon J. Blanchard:
RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues. 309-317 - Jiaxi Tang, Hongyi Wen, Ke Wang:
Revisiting Adversarially Learned Injection Attacks Against Recommender Systems. 318-327 - Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:
SSE-PT: Sequential Recommendation Via Personalized Transformer. 328-337 - Jin Peng Zhou, Zhaoyue Cheng, Felipe Pérez, Maksims Volkovs:
TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations. 338-347 - Sami Khenissi, Mariem Boujelbene, Olfa Nasraoui:
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System. 348-357 - Gourab K. Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly:
Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World. 358-367 - Bo-Wen Yuan, Yaxu Liu, Jui-Yang Hsia, Zhenhua Dong, Chih-Jen Lin:
Unbiased Ad Click Prediction for Position-aware Advertising Systems. 368-377 - Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma:
Unbiased Learning for the Causal Effect of Recommendation. 378-387 - Gustavo Penha, Claudia Hauff:
What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation. 388-397 - Tobias Schnabel, Gonzalo A. Ramos, Saleema Amershi:
"Who doesn't like dinosaurs?" Finding and Eliciting Richer Preferences for Recommendation. 398-407
Short Papers
- Fei Mi, Xiaoyu Lin, Boi Faltings:
ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation. 408-413 - Yagmur Gizem Cinar, Jean-Michel Renders:
Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation. 414-419 - Walid Bendada, Guillaume Salha, Théo Bontempelli:
Carousel Personalization in Music Streaming Apps with Contextual Bandits. 420-425 - Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei:
Causal Inference for Recommender Systems. 426-431 - Denis Kotkov, Qian Zhao, Kati Launis, Mats Neovius:
ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering. 432-437 - Francisco J. Peña, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, Aonghus Lawlor:
Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation. 438-443 - Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Anderson Soares, Renan M. Oliveira, Sandor Caetano:
Contextual Meta-Bandit for Recommender Systems Selection. 444-449 - Konstantina Christakopoulou, Madeleine Traverse, Trevor Potter, Emma Marriott, Daniel Li, Chris Haulk, Ed H. Chi, Minmin Chen:
Deconfounding User Satisfaction Estimation from Response Rate Bias. 450-455 - Dalin Guo, Sofia Ira Ktena, Pranay Kumar Myana, Ferenc Huszar, Wenzhe Shi, Alykhan Tejani, Michael Kneier, Sourav Das:
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. 456-461 - Kosetsu Tsukuda, Masataka Goto:
Explainable Recommendation for Repeat Consumption. 462-467 - Oren Barkan, Yonatan Fuchs, Avi Caciularu, Noam Koenigstein:
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering. 468-473 - Andres Ferraro, Dietmar Jannach, Xavier Serra:
Exploring Longitudinal Effects of Session-based Recommendations. 474-479 - Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Fit to Run: Personalised Recommendations for Marathon Training. 480-485 - Dmitri Goldenberg, Javier Albert, Lucas Bernardi, Pablo Estevez:
Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints. 486-491 - Baptiste Barreau, Laurent Carlier:
History-Augmented Collaborative Filtering for Financial Recommendations. 492-497 - Chu-Jen Shao, Hao-Ming Fu, Pu-Jen Cheng:
Improving One-class Recommendation with Multi-tasking on Various Preference Intensities. 498-502 - Andrés Villa, Vladimir Araujo, Francisca Cattan, Denis Parra:
Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games. 503-508 - Siyi Liu, Yujia Zheng:
Long-tail Session-based Recommendation. 509-514 - Sung Min Cho, Eunhyeok Park, Sungjoo Yoo:
MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation. 515-520 - Caojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, Wenzhe Shi:
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. 521-526 - Leyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov, Ivan V. Oseledets, Alexander Tuzhilin:
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks. 527-532 - Alessandro B. Melchiorre, Eva Zangerle, Markus Schedl:
Personality Bias of Music Recommendation Algorithms. 533-538 - Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners. 539-544 - Janhavi Dahihande, Akshay Jaiswal, Akshay Anil Pagar, Ajinkya Thakare, Magdalini Eirinaki, Iraklis Varlamis:
Reducing energy waste in households through real-time recommendations. 545-550 - Ziwei Zhu, Yun He, Yin Zhang, James Caverlee:
Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. 551-556 - Tushar Shandhilya, Nisheeth Srivastava:
Using conceptual incongruity as a basis for making recommendations. 557-561
Industry Papers
- Zachary A. Schendel, Faraz Farzin, Siddhi Sundar:
A Human Perspective on Algorithmic Similarity. 562 - Balázs Tóth, Sandhya Sachidanandan, Emil S. Jørgensen:
Balancing Relevance and Discovery to Inspire Customers in the IKEA App. 563 - Lakshmi Ramachandran:
Behavior-based Popularity Ranking on Amazon Video. 564-565 - R. Ramanathan, Nicolas K. Shinada, Sucheendra K. Palaniappan:
Building a reciprocal recommendation system at scale from scratch: Learnings from one of Japan's prominent dating applications. 566-567 - Zhenhua Dong, Hong Zhu, Pengxiang Cheng, Xinhua Feng, Guohao Cai, Xiuqiang He, Jun Xu, Jirong Wen:
Counterfactual learning for recommender system. 568-569 - Sanghamitra Deb:
Developing Recommendation System to provide a Personalized Learning experience at Chegg. 570 - Felipe Ferreira, Daniele R. Souza, Igor Moura, Matheus Barbieri, Hélio Côrtes Vieira Lopes:
Investigating Multimodal Features for Video Recommendations at Globoplay. 571-572 - Markus Reiter-Haas, David Wittenbrink, Emanuel Lacic:
On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student Career. 573-574 - Moumita Bhattacharya, Amey Barapatre:
Query as Context for Item-to-Item Recommendation. 575-576 - Jacopo Tagliabue, Bingqing Yu, Federico Bianchi:
The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference. 577-578
Demonstrations
- Ben Tan, Bo Liu, Vincent W. Zheng, Qiang Yang:
A Federated Recommender System for Online Services. 579-581 - Ting-Hsiang Wang, Xia Hu, Haifeng Jin, Qingquan Song, Xiaotian Han, Zirui Liu:
AutoRec: An Automated Recommender System. 582-584 - Rohan Anand, Joeran Beel:
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization. 585-587 - Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis, Siwei Liu, Yaxiong Wu, Xi Wang, Shangsong Liang, Yucheng Liang, Guangtao Zeng, Junhua Liang, Qiang Zhang:
BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems. 588-590 - Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier:
Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG. 591-593 - Nasim Sonboli, Robin Burke, Zijun Liu, Masoud Mansoury:
Fairness-aware Recommendation with librec-auto. 594-596 - Mete Sertkan, Julia Neidhardt, Hannes Werthner:
PicTouRe - A Picture-Based Tourism Recommender. 597-599 - Joeran Beel:
Recommender-Systems.com: A Central Platform for the Recommender-System Community. 600-603 - Aaron Rodden, Tarun Salh, Eriq Augustine, Lise Getoor:
VMI-PSL: Visual Model Inspector for Probabilistic Soft Logic. 604-606
Workshops & Challenge
- Michael D. Ekstrand, Pierre-Nicolas Schwab, Jean Garcia-Gathright, Toshihiro Kamishima, Nasim Sonboli:
3rd FAccTRec Workshop: Responsible Recommendation. 607-608 - Toine Bogers, Marijn Koolen, Casper Petersen, Bamshad Mobasher, Alexander Tuzhilin:
ComplexRec 2020: Workshop on Recommendation in Complex Environments. 609-610 - Alan Said, Hanna Schäfer, Helma Torkamaan, Christoph Trattner:
Fifth International Workshop on Health Recommender Systems (HealthRecSys 2020). 611-612 - Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen:
Interfaces and Human Decision Making for Recommender Systems. 613-618 - João Vinagre, Alípio Mário Jorge, Marie Al-Ghossein, Albert Bifet:
ORSUM - Workshop on Online Recommender Systems and User Modeling. 619-620 - Ching-Wei Chen, Longqi Yang, Hongyi Wen, Rosie Jones, Vladan Radosavljevic, Hugues Bouchard:
PodRecs: Workshop on Podcast Recommendations. 621-622 - Vito Walter Anelli, Amra Delic, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael M. Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, Frank Portman, Alykhan Tejani, Yuanpu Xie, Xiao Zhu, Wenzhe Shi:
RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter's Home Timeline. 623-627 - Thorsten Joachims, Yves Raimond, Olivier Koch, Maria Dimakopoulou, Flavian Vasile, Adith Swaminathan:
REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. 628-629 - Oren Sar Shalom, Dietmar Jannach, Joseph A. Konstan:
Second Workshop on the Impact of Recommender Systems at ACM RecSys '20. 630-631 - Shatha Jaradat, Nima Dokoohaki, Humberto Jesús Corona Pampín, Reza Shirvany:
Second Workshop on Recommender Systems in Fashion - fashionXrecsys2020. 632-634 - Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems. 635-637 - Antonela Tommasel, Daniela Godoy, Arkaitz Zubiaga:
Workshop on Online Misinformation- and Harm-Aware Recommender Systems. 638-639
Late-Breaking Results
- Samuel Alexander Stein, Gary M. Weiss, Yiwen Chen, Daniel D. Leeds:
A College Major Recommendation System. 640-644 - Yu Liu, Xiaoxiao Xu, Jincheng Wang, Yong Li, Changping Peng, Yongjun Bao, Weipeng P. Yan:
A Joint Dynamic Ranking System with DNN and Vector-based Clustering Bandit. 645-650 - Olivier Jeunen, Jan Van Balen, Bart Goethals:
Closed-Form Models for Collaborative Filtering with Side-Information. 651-656 - Heng-Shiou Sheu, Sheng Li:
Context-aware Graph Embedding for Session-based News Recommendation. 657-662 - Hyun Jeong Kim, So Yeon Park, Minju Park, Kyogu Lee:
Do Channels Matter? Illuminating Interpersonal Influence on Music Recommendations. 663-668 - Ashlee Milton, Levesson Batista, Garrett Allen, Siqi Gao, Yiu-Kai Ng, Maria Soledad Pera:
"Don't Judge a Book by its Cover": Exploring Book Traits Children Favor. 669-674 - Fábio Colaço, Márcia Barros, Francisco M. Couto:
DRecPy: A Python Framework for Developing Deep Learning-Based Recommenders. 675-680 - Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis:
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models. 681-686 - Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas:
Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions. 687-691 - Rishabh Mehrotra, Chirag Shah, Benjamin A. Carterette:
Investigating Listeners' Responses to Divergent Recommendations. 692-696 - Aaron Ng, Rishabh Mehrotra:
Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming Sessions. 697-702 - Ehtsham Elahi, Ashok Chandrashekar:
Learning Representations of Hierarchical Slates in Collaborative Filtering. 703-707 - Niall Twomey, Mikhail Fain, Andrey Ponikar, Nadine Sarraf:
Towards Multi-Language Recipe Personalisation and Recommendation. 708-713 - Shruti Kunde, Mayank Mishra, Amey Pandit, Rekha Singhal, Manoj Karunakaran Nambiar, Gautam Shroff, Shashank Gupta:
Recommending in changing times. 714-719 - Yong Li, Zihao Zhao, Zhiwei Fang, Kui Ma, Yafei Yao, Changping Peng, Yongjun Bao, Weipeng Yan:
Smart Targeting: A Relevance-driven and Configurable Targeting Framework for Advertising System. 720-725 - Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation. 726-731 - Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein:
Tuning Word2vec for Large Scale Recommendation Systems. 732-737
Tutorials
- Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
Adversarial Learning for Recommendation: Applications for Security and Generative Tasks - Concept to Code. 738-741 - David Rohde, Flavian Vasile, Sergey Ivanov, Otmane Sakhi:
Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation. 742-744 - Ruoyuan Gao, Chirag Shah:
Counteracting Bias and Increasing Fairness in Search and Recommender Systems. 745-747 - Andrea Barraza-Urbina, Dorota Glowacka:
Introduction to Bandits in Recommender Systems. 748-750 - Zuohui Fu, Yikun Xian, Yongfeng Zhang, Yi Zhang:
Tutorial on Conversational Recommendation Systems. 751-753 - Benedikt Schifferer, Chris Deotte, Even Oldridge:
Tutorial: Feature Engineering for Recommender Systems. 754-755
Doctoral Symposium
- Elizabeth Gómez:
Characterizing and Mitigating the Impact of Data Imbalance for Stakeholders in Recommender Systems. 756-757 - Andrea Iovine:
Conversational Agents for Recommender Systems. 758-763 - Jacob Munson:
Developing Work in Confidence, Similarity Structure, and Modeling User Event Time. 764-769 - Iulia Paun:
Efficiency-Effectiveness Trade-offs in Recommendation Systems. 770-775 - Bartolomé Ortiz Viso:
Evolutionary Approach in Recommendation Systems for Complex Structured Objects. 776-781 - Joey De Pauw:
Exploratory Methods for Evaluating Recommender Systems. 782-786 - Luchiana Cezara Brodeala:
Online Recommender system for Accessible Tourism Destinations. 787-791 - Pablo Pérez-Núñez:
Taking advantage of images and texts in recommender systems: semantics and explainability. 792-796
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-11-02 21:01 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
