Table of Contents

• Rude awakenings from behaviourist dreams. Methodological integrity and the GDPRMireille Hildebrandt. 1 [doi]
• Whose data traces, whose voices? Inequality in online participation and why it matters for recommendation systems researchEszter Hargittai. 2 [doi]
• Personalized re-ranking for recommendationChanghua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, Dan Pei. 3-11 [doi]
• Online ranking combinationErzsébet Frigó, Levente Kocsis. 12-19 [doi]
• A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendationXiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, Peng Jiang. 20-28 [doi]
• From preference into decision making: modeling user interactions in recommender systemsQian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan. 29-33 [doi]
• Deep generative ranking for personalized recommendationHuafeng Liu, Jingxuan Wen, Liping Jing, Jian Yu. 34-42 [doi]
• Recommending what video to watch next: a multitask ranking systemZhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed H. Chi. 43-51 [doi]
• Users in the loop: a psychologically-informed approach to similar item retrievalAmy A. Winecoff, Florin Brasoveanu, Bryce Casavant, Pearce Washabaugh, Matthew Graham. 52-59 [doi]
• Explaining and exploring job recommendations: a user-driven approach for interacting with knowledge-based job recommender systemsFrancisco Gutiérrez, Sven Charleer, Robin De Croon, Nyi Nyi Htun, Gerd Goetschalckx, Katrien Verbert. 60-68 [doi]
• Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systemsJaron Harambam, Dimitrios Bountouridis, Mykola Makhortykh, Joris Van Hoboken. 69-77 [doi]
• Efficient privacy-preserving recommendations based on social graphsAidmar Wainakh, Tim Grube, Jörg Daubert, Max Mühlhäuser. 78-86 [doi]
• PrivateJobMatch: a privacy-oriented deferred multi-match recommender system for stable employmentAmar Saini, Florin Rusu, Andrew Johnston. 87-95 [doi]
• User-centered evaluation of strategies for recommending sequences of points of interest to groupsDaniel Herzog, Wolfgang Wörndl. 96-100 [doi]
• Are we really making much progress? A worrying analysis of recent neural recommendation approachesMaurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach. 101-109 [doi]
• A deep learning system for predicting size and fit in fashion e-commerceAbdul-Saboor Sheikh, Romain Guigourès, Evgenii Koriagin, Yuen King Ho, Reza Shirvany, Roland Vollgraf, Urs Bergmann. 110-118 [doi]
• Relaxed softmax for PU learningUgo Tanielian, Flavian Vasile. 119-127 [doi]
• Style conditioned recommendationsMurium Iqbal, Kamelia Aryafar, Timothy Anderton. 128-136 [doi]
• Deep language-based critiquing for recommender systemsGa Wu, Kai Luo, Scott Sanner, Harold Soh. 137-145 [doi]
• Predictability limits in session-based next item recommendationPriit Järv. 146-150 [doi]
• A comparison of calibrated and intent-aware recommendationsMesut Kaya, Derek Bridge. 151-159 [doi]
• LORE: a large-scale offer recommendation engine with eligibility and capacity constraintsRahul Makhijani, Shreya Chakrabarti, Dale Struble, Yi Liu. 160-168 [doi]
• FiBiNET: combining feature importance and bilinear feature interaction for click-through rate predictionTongwen Huang, Zhiqi Zhang, Junlin Zhang. 169-177 [doi]
• Domain adaptation in display advertising: an application for partner cold-startKaran Aggarwal, Pranjul Yadav, S. Sathiya Keerthi. 178-186 [doi]
• Addressing delayed feedback for continuous training with neural networks in CTR predictionSofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszár, Steven Yoo, Wenzhe Shi. 187-195 [doi]
• Ghosting: contextualized inline query completion in large scale retail searchLakshmi Ramachandran, Uma Murthy. 196-200 [doi]
• Collective embedding for neural context-aware recommender systemsFelipe Soares Da Costa, Peter Dolog. 201-209 [doi]
• A recommender system for heterogeneous and time sensitive environmentMeng Wu, Ying Zhu, Qilian Yu, Bhargav Rajendra, Yunqi Zhao, Navid Aghdaie, Kazi A. Zaman. 210-218 [doi]
• Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systemsJames Neve, Ivan Palomares. 219-227 [doi]
• CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendationsOren Barkan, Noam Koenigstein, Eylon Yogev, Ori Katz. 228-236 [doi]
• Online learning to rank for sequential music recommendationBruno L. Pereira, Alberto Ueda, Gustavo Penha, Rodrygo L. T. Santos, Nivio Ziviani. 237-245 [doi]
• Pace my race: recommendations for marathon runningJakim Berndsen, Barry Smyth, Aonghus Lawlor. 246-250 [doi]
• Efficient similarity computation for collaborative filtering in dynamic environmentsOlivier Jeunen, Koen Verstrepen, Bart Goethals. 251-259 [doi]
• Personalized diffusions for top-n recommendationAthanasios N. Nikolakopoulos, Dimitris Berberidis, George Karypis, Georgios B. Giannakis. 260-268 [doi]
• Sampling-bias-corrected neural modeling for large corpus item recommendationsXinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, Ed H. Chi. 269-277 [doi]
• Leveraging post-click feedback for content recommendationsHongyi Wen, Longqi Yang, Deborah Estrin. 278-286 [doi]
• When actions speak louder than clicks: a combined model of purchase probability and long-term customer satisfactionGal Lavee, Noam Koenigstein, Oren Barkan. 287-295 [doi]
• Uplift-based evaluation and optimization of recommendersMasahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma. 296-304 [doi]
• Deep social collaborative filteringWenqi Fan, Yao Ma 0001, Dawei Yin, Jianping Wang, Jiliang Tang, Qing Li. 305-313 [doi]
• Attribute-aware non-linear co-embeddings of graph featuresAhmed Rashed, Josif Grabocka, Lars Schmidt-Thieme. 314-321 [doi]
• Adversarial attacks on an oblivious recommenderKonstantina Christakopoulou, Arindam Banerjee. 322-330 [doi]
• HybridSVD: when collaborative information is not enoughEvgeny Frolov, Ivan V. Oseledets. 331-339 [doi]
• Variational low rank multinomials for collaborative filtering with side-informationEhtsham Elahi, Wei Wang, Dave Ray, Aish Fenton, Tony Jebara. 340-347 [doi]
• Quick and accurate attack detection in recommender systems through user attributesMehmet Aktukmak, Yasin Yilmaz, Ismail Uysal. 348-352 [doi]
• A generative model for review-based recommendationsOren Sar Shalom, Guy Uziel, Amir Kantor. 353-357 [doi]
• A simple multi-armed nearest-neighbor bandit for interactive recommendationJavier Sanz-Cruzado, Pablo Castells, Esther López. 358-362 [doi]
• Adversarial tensor factorization for context-aware recommendationHuiyuan Chen, Jing Li. 363-367 [doi]
• Aligning daily activities with personality: towards a recommender system for improving wellbeingMohammed Khwaja, Miquel Ferrer, Jesus Omana Iglesias, A. Aldo Faisal, Aleksandar Matic. 368-372 [doi]
• Asymmetric Bayesian personalized ranking for one-class collaborative filteringShan Ouyang, Lin Li, Weike Pan, Zhong Ming 0001. 373-377 [doi]
• Attribute-based evaluation for recommender systems: incorporating user and item attributes in evaluation metricsPablo Sánchez 0001, Alejandro Bellogín. 378-382 [doi]
• Combining text summarization and aspect-based sentiment analysis of users' reviews to justify recommendationsCataldo Musto, Gaetano Rossiello, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. 383-387 [doi]
• Compositional network embedding for link predictionTianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang. 388-392 [doi]
• Data mining for item recommendation in MOBA gamesVladimir Araujo, Felipe Rios, Denis Parra. 393-397 [doi]
• DualDiv: diversifying items and explanation styles in explainable hybrid recommendationKosetsu Tsukuda, Masataka Goto. 398-402 [doi]
• Enhancing VAEs for collaborative filtering: flexible priors & gating mechanismsDaeryong Kim, Bongwon Suh. 403-407 [doi]
• Find my next job: labor market recommendations using administrative big dataSnorre S. Frid-Nielsen. 408-412 [doi]
• Greedy optimized multileaving for personalizationKojiro Iizuka, Takeshi Yoneda, Yoshifumi Seki. 413-417 [doi]
• Guiding creative design in online advertisingShaunak Mishra, Manisha Verma, Jelena Gligorijevic. 418-422 [doi]
• How can they know that?: a study of factors affecting the creepiness of recommendationsHelma Torkamaan, Catalin Mihai Barbu, Jürgen Ziegler 0001. 423-427 [doi]
• Latent multi-criteria ratings for recommendationsPan Li, Alexander Tuzhilin. 428-431 [doi]
• Multi-armed recommender system bandit ensemblesRocío Cañamares, Marcos Redondo, Pablo Castells. 432-436 [doi]
• Music recommendations in hyperbolic space: an application of empirical bayes and hierarchical poincaré embeddingsTimothy Schmeier, Joseph Chisari, Sam Garrett, Brett Vintch. 437-441 [doi]
• On gossip-based information dissemination in pervasive recommender systemsTobias Eichinger, Felix Beierle, Robin Papke, Lucas Rebscher, Hong Chinh Tran, Magdalena Trzeciak. 442-446 [doi]
• On the discriminative power of hyper-parameters in cross-validation and how to choose themVito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, Azzurra Ragone. 447-451 [doi]
• PAL: a position-bias aware learning framework for CTR prediction in live recommender systemsHuifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang. 452-456 [doi]
• PDMFRec: a decentralised matrix factorisation with tunable user-centric privacyErika Duriakova, Elias Z. Tragos, Barry Smyth, Neil Hurley, Francisco J. Peña, Panagiotis Symeonidis, James Geraci, Aonghus Lawlor. 457-461 [doi]
• Performance comparison of neural and non-neural approaches to session-based recommendationMalte Ludewig, Noemi Mauro, Sara Latifi, Dietmar Jannach. 462-466 [doi]
• Personalized fairness-aware re-ranking for microlendingWeiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, Shengyu Zhang. 467-471 [doi]
• Pick & merge: an efficient item filtering scheme for Windows store recommendationsAdi Makmal, Jonathan Ephrath, Hilik Berezin, Liron Allerhand, Nir Nice, Noam Koenigstein. 472-476 [doi]
• Predicting online performance of job recommender systems with offline evaluationAdrien Mogenet, Tuan-Anh Nguyen Pham, Masahiro Kazama, Jialin Kong. 477-480 [doi]
• Predicting user routines with masked dilated convolutionsRenzhong Wang, Dragomir Yankov, Michael R. Evans, Senthil Palanisamy, Siddhartha Arora, Wei Wu. 481-485 [doi]
• Product collection recommendation in online retailPigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Nian Yan, Unaiza Ahsan, Khalifeh Al Jadda, Huiming Qu. 486-490 [doi]
• PyRecGym: a reinforcement learning gym for recommender systemsBichen Shi, Makbule Gulcin Ozsoy, Neil Hurley, Barry Smyth, Elias Z. Tragos, James Geraci, Aonghus Lawlor. 491-495 [doi]
• Should we embed?: a study on the online performance of utilizing embeddings for real-time job recommendationsEmanuel Lacic, Markus Reiter-Haas, Tomislav Duricic, Valentin Slawicek, Elisabeth Lex. 496-500 [doi]
• The influence of personal values on music taste: towards value-based music recommendationsSandy Manolios, Alan Hanjalic, Cynthia C. S. Liem. 501-505 [doi]
• Time slice imputation for personalized goal-based recommendation in higher educationWeijie Jiang, Zachary A. Pardos. 506-510 [doi]
• Traversing semantically annotated queries for task-oriented query recommendationArthur Câmara, Rodrygo L. T. Santos. 511-515 [doi]
• User-centric evaluation of session-based recommendations for an automated radio stationMalte Ludewig, Dietmar Jannach. 516-520 [doi]
• Using AI to build communities around interests on LinkedInAbdulla Al-Qawasmeh, Ankan Saha. 521 [doi]
• The trinity of luxury fashion recommendations: data, experts and experimentationAna Rita Magalhães. 522 [doi]
• "Just play something awesome": the personalization powering voice interactions at PandoraVito Claudio Ostuni. 523 [doi]
• Future of in-vehicle recommendation systems @ BoschJuergen Luettin, Susanne Rothermel, Mark Andrew. 524 [doi]
• Designer-driven add-to-cart recommendationsSandhya Sachidanandan, Richard Luong, Emil S. Joergensen. 525 [doi]
• Groupon finally explains why we showed those offersSasank Channapragada, Harshit Syal, Ibrahim Maali. 526 [doi]
• Homepage personalization at spotifyOguz Semerci, Alois Gruson, Catherinee Edwards, Ben Lacker, Clay Gibson, Vladan Radosavljevic. 527 [doi]
• Recommendation in home improvement industry, challenges and opportunitiesKhalifeh Al Jadda. 528 [doi]
• Recommendation systems compliant with legal and editorial policies: the BBC+ app journeyMaria Panteli. 529 [doi]
• Incorporating intent propensities in personalized next best action recommendationYuxi Zhang, Kexin Xie. 530 [doi]
• Driving content recommendations by building a knowledge base using weak supervision and transfer learningSanghamitra Deb. 531 [doi]
• AnnoMath TeX - a formula identifier annotation recommender system for STEM documentsPhilipp Scharpf, Ian Mackerracher, Moritz Schubotz, Jöran Beel, Corinna Breitinger, Bela Gipp. 532-533 [doi]
• Darwin & Goliath: a white-label recommender-system as-a-service with automated algorithm-selectionJöran Beel, Alan Griffin, Conor O'Shea. 534-535 [doi]
• FineNet: a joint convolutional and recurrent neural network model to forecast and recommend anomalous financial itemsYu-Che Tsai, Chih-Yao Chen, Shao-Lun Ma, Pei-Chi Wang, You-Jia Chen, Yu-Chieh Chang, Cheng-Te Li. 536-537 [doi]
• Interactive evaluation of recommender systems with SNIPER: an episode mining approachSandy Moens, Olivier Jeunen, Bart Goethals. 538-539 [doi]
• IRF: interactive recommendation through dialogueOznur Alkan, Massimiliano Mattetti, Elizabeth M. Daly, Adi Botea, Inge Vejsbjerg. 540-541 [doi]
• Microsoft recommenders: tools to accelerate developing recommender systemsScott Graham, Jun-Ki Min, Tao Wu. 542-543 [doi]
• StoryTime: eliciting preferences from children for book recommendationsAshlee Milton, Michael Green, Adam Keener, Joshua Ames, Michael D. Ekstrand, Maria Soledad Pera. 544-545 [doi]
• Towards interactive recommending in model-based collaborative filtering systemsBenedikt Loepp, Jürgen Ziegler 0001. 546-547 [doi]
• Workshop on context-aware recommender systemsGediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci 0001, Alexander Tuzhilin, Moshe Unger. 548-549 [doi]
• Third workshop on recommendation in complex scenarios (ComplexRec 2019)Marijn Koolen, Toine Bogers, Bamshad Mobasher, Alexander Tuzhilin. 550-551 [doi]
• Workshop on recommender systems in fashion (fashionXrecsys2019)Shatha Jaradat, Nima Dokoohaki, Humberto Jesús Corona Pampín, Reza Shirvany. 552-553 [doi]
• Fourth international workshop on health recommender systems (HealthRecSys 2019)David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schäfer, Helma Torkamaan, Christoph Trattner. 554-555 [doi]
• First workshop on the impact of recommender systems at ACM RecSys 2019Oren Sar Shalom, Dietmar Jannach, Ido Guy. 556-557 [doi]
• The 7th international workshop on news recommendation and analytics (INRA 2019)Özlem Özgöbek, Benjamin Kille, Jon Atle Gulla, Andreas Lommatzsch. 558-559 [doi]
• RecSys '19 joint workshop on interfaces and human decision making for recommender systemsPeter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen. 560-561 [doi]
• ORSUM 2019 2nd workshop on online recommender systems and user modelingJoão Vinagre, Alípio Mário Jorge, Albert Bifet, Marie Al-Ghossein. 562-563 [doi]
• RecTour 2019: workshop on recommenders in tourismJulia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Markus Zanker, Catalin Mihai Barbu. 564-565 [doi]
• Recommendation in multistakeholder environmentsRobin Burke, Himan Abdollahpouri, Edward C. Malthouse, K. P. Thai, Yongfeng Zhang. 566-567 [doi]
• REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendationThorsten Joachims, Maria Dimakopoulou, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile. 568-569 [doi]
• RecSys challenge 2019: session-based hotel recommendationsPeter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard Paul Leyson, Philipp Monreal. 570-571 [doi]
• ACM RecSys'19 late-breaking results (posters)Marko Tkalcic, Maria Soledad Pera. 572-573 [doi]
• Bandit algorithms in recommender systemsDorota Glowacka. 574-575 [doi]
• Fairness and discrimination in recommendation and retrievalMichael D. Ekstrand, Robin Burke, Fernando Diaz. 576-577 [doi]
• Multi-stakeholder recommendations: case studies, methods and challengesYong Zheng. 578-579 [doi]
• Recommendations in a marketplaceRishabh Mehrotra, Benjamin A. Carterette. 580-581 [doi]
• SMORe: modularize graph embedding for recommendationChih-Ming Chen 0003, Ting-Hsiang Wang, Chuan-Ju Wang, Ming-Feng Tsai. 582-583 [doi]
• Concept to code: deep learning for multitask recommendationOmprakash Sonie. 584-585 [doi]
• Music cold-start and long-tail recommendation: bias in deep representationsAndres Ferraro. 586-590 [doi]
• User's activity driven short-term context inferenceMiroslav Rac. 591-595 [doi]
• Revisiting offline evaluation for implicit-feedback recommender systemsOlivier Jeunen. 596-600 [doi]
• Exploiting contextual information for recommender systems oriented to tourismPablo Sánchez 0001. 601-605 [doi]
• Recommender systems for contextually-aware, versioned itemsYayu Zhou. 606-610 [doi]
• Recommender system for developing new preferences and goalsYu Liang. 611-615 [doi]