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ORCIDRecSys 2019: Copenhagen, Denmark
- Toine Bogers, Alan Said, Peter Brusilovsky, Domonkos Tikk:
Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019. ACM 2019, ISBN 978-1-4503-6243-6
Invited keynotes
- Mireille Hildebrandt:
Rude awakenings from behaviourist dreams. Methodological integrity and the GDPR. 1 - Eszter Hargittai:
Whose data traces, whose voices? Inequality in online participation and why it matters for recommendation systems research. 2
Ranking and deep learning in recommenders
- Changhua Pei, Yi Zhang
, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, Dan Pei:
Personalized re-ranking for recommendation. 3-11 - Erzsébet Frigó, Levente Kocsis:
Online ranking combination. 12-19 - Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, Peng Jiang:
A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. 20-28 - Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan:
From preference into decision making: modeling user interactions in recommender systems. 29-33 - Huafeng Liu, Jingxuan Wen, Liping Jing, Jian Yu:
Deep generative ranking for personalized recommendation. 34-42 - Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed H. Chi:
Recommending what video to watch next: a multitask ranking system. 43-51
User side of recommender systems
- Amy A. Winecoff, Florin Brasoveanu, Bryce Casavant, Pearce Washabaugh, Matthew Graham:
Users in the loop: a psychologically-informed approach to similar item retrieval. 52-59 - Francisco Gutiérrez, Sven Charleer, Robin De Croon, Nyi Nyi Htun, Gerd Goetschalckx, Katrien Verbert:
Explaining and exploring job recommendations: a user-driven approach for interacting with knowledge-based job recommender systems. 60-68 - Jaron Harambam, Dimitrios Bountouridis, Mykola Makhortykh, Joris Van Hoboken:
Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems. 69-77 - Aidmar Wainakh, Tim Grube, Jörg Daubert, Max Mühlhäuser:
Efficient privacy-preserving recommendations based on social graphs. 78-86 - Amar Saini, Florin Rusu, Andrew Johnston:
PrivateJobMatch: a privacy-oriented deferred multi-match recommender system for stable employment. 87-95 - Daniel Herzog, Wolfgang Wörndl:
User-centered evaluation of strategies for recommending sequences of points of interest to groups. 96-100
Deep learning for recommender systems
- Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Are we really making much progress? A worrying analysis of recent neural recommendation approaches. 101-109 - Abdul-Saboor Sheikh, Romain Guigourès, Evgenii Koriagin, Yuen King Ho, Reza Shirvany, Roland Vollgraf, Urs Bergmann:
A deep learning system for predicting size and fit in fashion e-commerce. 110-118 - Ugo Tanielian, Flavian Vasile:
Relaxed softmax for PU learning. 119-127 - Murium Iqbal, Kamelia Aryafar, Timothy Anderton:
Style conditioned recommendations. 128-136 - Ga Wu, Kai Luo, Scott Sanner, Harold Soh:
Deep language-based critiquing for recommender systems. 137-145 - Priit Järv:
Predictability limits in session-based next item recommendation. 146-150
Recommendation in advertising, promotions, intent and search
- Mesut Kaya, Derek G. Bridge:
A comparison of calibrated and intent-aware recommendations. 151-159 - Rahul Makhijani, Shreya Chakrabarti, Dale Struble, Yi Liu:
LORE: a large-scale offer recommendation engine with eligibility and capacity constraints. 160-168 - Tongwen Huang, Zhiqi Zhang, Junlin Zhang:
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. 169-177 - Karan Aggarwal, Pranjul Yadav, S. Sathiya Keerthi:
Domain adaptation in display advertising: an application for partner cold-start. 178-186 - Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszár, Steven Yoo, Wenzhe Shi:
Addressing delayed feedback for continuous training with neural networks in CTR prediction. 187-195 - Lakshmi Ramachandran, Uma Murthy:
Ghosting: contextualized inline query completion in large scale retail search. 196-200
Application of recommenders in personal needs
- Felipe Soares Da Costa, Peter Dolog:
Collective embedding for neural context-aware recommender systems. 201-209 - Meng Wu, Ying Zhu, Qilian Yu, Bhargav Rajendra, Yunqi Zhao, Navid Aghdaie, Kazi A. Zaman:
A recommender system for heterogeneous and time sensitive environment. 210-218 - James Neve, Ivan Palomares:
Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems. 219-227 - Oren Barkan, Noam Koenigstein, Eylon Yogev, Ori Katz:
CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations. 228-236 - Bruno L. Pereira, Alberto Ueda, Gustavo Penha, Rodrygo L. T. Santos, Nivio Ziviani:
Online learning to rank for sequential music recommendation. 237-245 - Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Pace my race: recommendations for marathon running. 246-250
Algorithms: Large-scale, constraints and evaluation
- Olivier Jeunen, Koen Verstrepen, Bart Goethals:
Efficient similarity computation for collaborative filtering in dynamic environments. 251-259 - Athanasios N. Nikolakopoulos, Dimitris Berberidis, George Karypis, Georgios B. Giannakis:
Personalized diffusions for top-n recommendation. 260-268 - Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, Ed H. Chi:
Sampling-bias-corrected neural modeling for large corpus item recommendations. 269-277 - Hongyi Wen, Longqi Yang, Deborah Estrin:
Leveraging post-click feedback for content recommendations. 278-286 - Gal Lavee, Noam Koenigstein, Oren Barkan:
When actions speak louder than clicks: a combined model of purchase probability and long-term customer satisfaction. 287-295 - Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma:
Uplift-based evaluation and optimization of recommenders. 296-304
Using side-information and user attributes and cold-start in recommender algorithms
- Wenqi Fan, Yao Ma, Dawei Yin, Jianping Wang, Jiliang Tang, Qing Li:
Deep social collaborative filtering. 305-313 - Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme:
Attribute-aware non-linear co-embeddings of graph features. 314-321 - Konstantina Christakopoulou, Arindam Banerjee:
Adversarial attacks on an oblivious recommender. 322-330 - Evgeny Frolov, Ivan V. Oseledets:
HybridSVD: when collaborative information is not enough. 331-339 - Ehtsham Elahi, Wei Wang, Dave Ray, Aish Fenton, Tony Jebara:
Variational low rank multinomials for collaborative filtering with side-information. 340-347 - Mehmet Aktukmak, Yasin Yilmaz, Ismail Uysal:
Quick and accurate attack detection in recommender systems through user attributes. 348-352
Short papers with poster presentation
- Oren Sar Shalom, Guy Uziel, Amir Kantor:
A generative model for review-based recommendations. 353-357 - Javier Sanz-Cruzado, Pablo Castells, Esther López:
A simple multi-armed nearest-neighbor bandit for interactive recommendation. 358-362 - Huiyuan Chen, Jing Li:
Adversarial tensor factorization for context-aware recommendation. 363-367 - Mohammed Khwaja, Miquel Ferrer, Jesús Omana Iglesias, A. Aldo Faisal, Aleksandar Matic:
Aligning daily activities with personality: towards a recommender system for improving wellbeing. 368-372 - Shan Ouyang, Lin Li, Weike Pan, Zhong Ming:
Asymmetric Bayesian personalized ranking for one-class collaborative filtering. 373-377 - Pablo Sánchez, Alejandro Bellogín:
Attribute-based evaluation for recommender systems: incorporating user and item attributes in evaluation metrics. 378-382 - Cataldo Musto, Gaetano Rossiello, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro:
Combining text summarization and aspect-based sentiment analysis of users' reviews to justify recommendations. 383-387 - Tianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang:
Compositional network embedding for link prediction. 388-392 - Vladimir Araujo, Felipe Rios, Denis Parra:
Data mining for item recommendation in MOBA games. 393-397 - Kosetsu Tsukuda, Masataka Goto:
DualDiv: diversifying items and explanation styles in explainable hybrid recommendation. 398-402 - Daeryong Kim, Bongwon Suh:
Enhancing VAEs for collaborative filtering: flexible priors & gating mechanisms. 403-407 - Snorre S. Frid-Nielsen:
Find my next job: labor market recommendations using administrative big data. 408-412 - Kojiro Iizuka, Takeshi Yoneda, Yoshifumi Seki:
Greedy optimized multileaving for personalization. 413-417 - Shaunak Mishra, Manisha Verma, Jelena Gligorijevic:
Guiding creative design in online advertising. 418-422 - Helma Torkamaan, Catalin-Mihai Barbu, Jürgen Ziegler:
How can they know that?: a study of factors affecting the creepiness of recommendations. 423-427 - Pan Li, Alexander Tuzhilin:
Latent multi-criteria ratings for recommendations. 428-431 - Rocío Cañamares, Marcos Redondo, Pablo Castells:
Multi-armed recommender system bandit ensembles. 432-436 - Timothy Schmeier, Joseph Chisari, Sam Garrett, Brett Vintch:
Music recommendations in hyperbolic space: an application of empirical bayes and hierarchical poincaré embeddings. 437-441 - Tobias Eichinger, Felix Beierle, Robin Papke, Lucas Rebscher, Hong Chinh Tran, Magdalena Trzeciak:
On gossip-based information dissemination in pervasive recommender systems. 442-446 - Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, Azzurra Ragone:
On the discriminative power of hyper-parameters in cross-validation and how to choose them. 447-451 - Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang:
PAL: a position-bias aware learning framework for CTR prediction in live recommender systems. 452-456 - Erika Duriakova, Elias Z. Tragos, Barry Smyth, Neil Hurley, Francisco J. Peña, Panagiotis Symeonidis, James Geraci, Aonghus Lawlor:
PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy. 457-461 - Malte Ludewig, Noemi Mauro, Sara Latifi, Dietmar Jannach:
Performance comparison of neural and non-neural approaches to session-based recommendation. 462-466 - Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, Shengyu Zhang:
Personalized fairness-aware re-ranking for microlending. 467-471 - Adi Makmal, Jonathan Ephrath, Hilik Berezin, Liron I. Allerhand, Nir Nice, Noam Koenigstein:
Pick & merge: an efficient item filtering scheme for Windows store recommendations. 472-476 - Adrien Mogenet, Tuan-Anh Nguyen Pham, Masahiro Kazama, Jialin Kong:
Predicting online performance of job recommender systems with offline evaluation. 477-480 - Renzhong Wang, Dragomir Yankov, Michael R. Evans, Senthil Palanisamy, Siddhartha Arora, Wei Wu:
Predicting user routines with masked dilated convolutions. 481-485 - Pigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Nian Yan, Unaiza Ahsan, Khalifeh Al Jadda, Huiming Qu:
Product collection recommendation in online retail. 486-490 - Bichen Shi, Makbule Gulcin Ozsoy, Neil Hurley, Barry Smyth, Elias Z. Tragos, James Geraci, Aonghus Lawlor:
PyRecGym: a reinforcement learning gym for recommender systems. 491-495 - Emanuel Lacic, Markus Reiter-Haas, Tomislav Duricic, Valentin Slawicek, Elisabeth Lex:
Should we embed?: a study on the online performance of utilizing embeddings for real-time job recommendations. 496-500 - Sandy Manolios, Alan Hanjalic, Cynthia C. S. Liem:
The influence of personal values on music taste: towards value-based music recommendations. 501-505 - Weijie Jiang, Zachary A. Pardos:
Time slice imputation for personalized goal-based recommendation in higher education. 506-510 - Arthur Câmara, Rodrygo L. T. Santos:
Traversing semantically annotated queries for task-oriented query recommendation. 511-515 - Malte Ludewig, Dietmar Jannach:
User-centric evaluation of session-based recommendations for an automated radio station. 516-520
Novel uses of recommenders
- Abdulla Al-Qawasmeh, Ankan Saha:
Using AI to build communities around interests on LinkedIn. 521 - Ana Rita Magalhães:
The trinity of luxury fashion recommendations: data, experts and experimentation. 522 - Vito Claudio Ostuni:
"Just play something awesome": the personalization powering voice interactions at Pandora. 523 - Juergen Luettin, Susanne Rothermel, Mark Andrew:
Future of in-vehicle recommendation systems @ Bosch. 524 - Sandhya Sachidanandan, Richard Luong, Emil S. Joergensen:
Designer-driven add-to-cart recommendations. 525
Novel approaches to recommenders
- Sasank Channapragada, Harshit Syal, Ibrahim Maali:
Groupon finally explains why we showed those offers. 526 - Oguz Semerci, Alois Gruson, Catherine M. Edwards, Ben Lacker, Clay Gibson, Vladan Radosavljevic:
Homepage personalization at spotify. 527 - Khalifeh Al Jadda:
Recommendation in home improvement industry, challenges and opportunities. 528 - Maria Panteli:
Recommendation systems compliant with legal and editorial policies: the BBC+ app journey. 529 - Yuxi Zhang, Kexin Xie:
Incorporating intent propensities in personalized next best action recommendation. 530 - Sanghamitra Deb:
Driving content recommendations by building a knowledge base using weak supervision and transfer learning. 531
Demonstrations
- Philipp Scharpf, Ian Mackerracher, Moritz Schubotz, Jöran Beel, Corinna Breitinger, Bela Gipp:
AnnoMath TeX - a formula identifier annotation recommender system for STEM documents. 532-533 - Jöran Beel, Alan Griffin, Conor O'Shea:
Darwin & Goliath: a white-label recommender-system as-a-service with automated algorithm-selection. 534-535 - Yu-Che Tsai, Chih-Yao Chen, Shao-Lun Ma, Pei-Chi Wang, You-Jia Chen, Yu-Chieh Chang, Cheng-Te Li:
FineNet: a joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items. 536-537 - Sandy Moens, Olivier Jeunen, Bart Goethals:
Interactive evaluation of recommender systems with SNIPER: an episode mining approach. 538-539 - Oznur Alkan, Massimiliano Mattetti, Elizabeth M. Daly, Adi Botea, Inge Vejsbjerg:
IRF: interactive recommendation through dialogue. 540-541 - Scott Graham, Jun-Ki Min, Tao Wu:
Microsoft recommenders: tools to accelerate developing recommender systems. 542-543 - Ashlee Milton, Michael Green, Adam Keener, Joshua Ames, Michael D. Ekstrand, Maria Soledad Pera:
StoryTime: eliciting preferences from children for book recommendations. 544-545 - Benedikt Loepp, Jürgen Ziegler:
Towards interactive recommending in model-based collaborative filtering systems. 546-547
Workshops, challenge, and late-breaking results
- Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on context-aware recommender systems. 548-549 - Marijn Koolen, Toine Bogers, Bamshad Mobasher, Alexander Tuzhilin:
Third workshop on recommendation in complex scenarios (ComplexRec 2019). 550-551 - Shatha Jaradat, Nima Dokoohaki, Humberto Jesús Corona Pampín, Reza Shirvany:
Workshop on recommender systems in fashion (fashionXrecsys2019). 552-553 - David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schäfer, Helma Torkamaan, Christoph Trattner:
Fourth international workshop on health recommender systems (HealthRecSys 2019). 554-555 - Oren Sar Shalom, Dietmar Jannach, Ido Guy:
First workshop on the impact of recommender systems at ACM RecSys 2019. 556-557 - Özlem Özgöbek, Benjamin Kille, Jon Atle Gulla, Andreas Lommatzsch:
The 7th international workshop on news recommendation and analytics (INRA 2019). 558-559 - Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen:
RecSys '19 joint workshop on interfaces and human decision making for recommender systems. 560-561 - João Vinagre, Alípio Mário Jorge, Albert Bifet, Marie Al-Ghossein:
ORSUM 2019 2nd workshop on online recommender systems and user modeling. 562-563 - Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Markus Zanker, Catalin-Mihai Barbu:
RecTour 2019: workshop on recommenders in tourism. 564-565 - Robin Burke, Himan Abdollahpouri, Edward C. Malthouse, K. P. Thai, Yongfeng Zhang:
Recommendation in multistakeholder environments. 566-567 - Thorsten Joachims, Maria Dimakopoulou, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile:
REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation. 568-569 - Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard Paul Leyson, Philipp Monreal:
RecSys challenge 2019: session-based hotel recommendations. 570-571 - Marko Tkalcic, Maria Soledad Pera:
ACM RecSys'19 late-breaking results (posters). 572-573
Tutorials
- Dorota Glowacka:
Bandit algorithms in recommender systems. 574-575 - Michael D. Ekstrand, Robin Burke, Fernando Diaz:
Fairness and discrimination in recommendation and retrieval. 576-577 - Yong Zheng:
Multi-stakeholder recommendations: case studies, methods and challenges. 578-579 - Rishabh Mehrotra, Benjamin A. Carterette:
Recommendations in a marketplace. 580-581 - Chih-Ming Chen, Ting-Hsiang Wang, Chuan-Ju Wang, Ming-Feng Tsai:
SMORe: modularize graph embedding for recommendation. 582-583 - Omprakash Sonie:
Concept to code: deep learning for multitask recommendation. 584-585
Doctoral symposium
- Andres Ferraro:
Music cold-start and long-tail recommendation: bias in deep representations. 586-590 - Miroslav Rac:
User's activity driven short-term context inference. 591-595 - Olivier Jeunen:
Revisiting offline evaluation for implicit-feedback recommender systems. 596-600 - Pablo Sánchez:
Exploiting contextual information for recommender systems oriented to tourism. 601-605 - Yayu Zhou:
Recommender systems for contextually-aware, versioned items. 606-610 - Yu Liang:
Recommender system for developing new preferences and goals. 611-615
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