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ORCIDRecSys 2018: Vancouver, BC, Canada
- Sole Pera, Michael D. Ekstrand, Xavier Amatriain, John O'Donovan:
Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2-7, 2018. ACM 2018, ISBN 978-1-4503-5901-6
Invited keynotes
- Elizabeth F. Churchill:
Five E's: reflecting on the design of recommendations. 1 - Lise Getoor:
Scalable structured prediction for richly structured socio-behavioral data. 2 - Christopher Berry:
Recommending social cohesion. 3
Why did i get this? explaining recommendations
- Yichao Lu, Ruihai Dong, Barry Smyth:
Why I like it: multi-task learning for recommendation and explanation. 4-12 - Yucheng Jin
, Nava Tintarev, Katrien Verbert:
Effects of personal characteristics on music recommender systems with different levels of controllability. 13-21 - Akiva Kleinerman, Ariel Rosenfeld, Sarit Kraus:
Providing explanations for recommendations in reciprocal environments. 22-30 - James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, Rishabh Mehrotra:
Explore, exploit, and explain: personalizing explainable recommendations with bandits. 31-39 - Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan:
Interpreting user inaction in recommender systems. 40-48 - Benedikt Loepp, Tim Donkers, Timm Kleemann, Jürgen Ziegler:
Impact of item consumption on assessment of recommendations in user studies. 49-53
From browser to buyer: online product recommendations
- Özge Sürer, Robin Burke, Edward C. Malthouse:
Multistakeholder recommendation with provider constraints. 54-62 - Rajiv Pasricha, Julian J. McAuley:
Translation-based factorization machines for sequential recommendation. 63-71 - Hongyi Wen, Longqi Yang, Michael Sobolev, Deborah Estrin:
Exploring recommendations under user-controlled data filtering. 72-76 - Yin Zhang, Haokai Lu, Wei Niu, James Caverlee:
Quality-aware neural complementary item recommendation. 77-85 - Mengting Wan, Julian J. McAuley:
Item recommendation on monotonic behavior chains. 86-94 - Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang:
Deep reinforcement learning for page-wise recommendations. 95-103 - Stephen Bonner, Flavian Vasile:
Causal embeddings for recommendation. 104-112
Learning and optimization
- Dong Deng, Liping Jing, Jian Yu, Shaolong Sun, Haofei Zhou:
Neural gaussian mixture model for review-based rating prediction. 113-121 - Yilin Shen, Yue Deng, Avik Ray, Hongxia Jin:
Interactive recommendation via deep neural memory augmented contextual bandits. 122-130 - Akiva Kleinerman, Ariel Rosenfeld, Francesco Ricci, Sarit Kraus:
Optimally balancing receiver and recommended users' importance in reciprocal recommender systems. 131-139 - Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai:
HOP-rec: high-order proximity for implicit recommendation. 140-144 - Thanh Vinh Vo, Harold Soh:
Generation meets recommendation: proposing novel items for groups of users. 145-153 - Harald Steck:
Calibrated recommendations. 154-162
Travel and entertainment
- Paolo Dragone, Giovanni Pellegrini, Michele Vescovi, Katya Tentori, Andrea Passerini:
No more ready-made deals: constructive recommendation for telco service bundling. 163-171 - Anna Sepliarskaia, Julia Kiseleva, Filip Radlinski, Maarten de Rijke:
Preference elicitation as an optimization problem. 172-180 - Rohit Verma, Surjya Ghosh, Saketh Mahankali, Niloy Ganguly, Bivas Mitra, Sandip Chakraborty:
Comfride: a smartphone based system for comfortable public transport recommendation. 181-189 - Longqi Yang, Michael Sobolev, Christina Tsangouri, Deborah Estrin:
Understanding user interactions with podcast recommendations delivered via voice. 190-194 - Bobby Prévost, Jonathan Laflamme Janssen, Jaime R. Camacaro, Carolina Bessega:
Deep inventory time translation to improve recommendations for real-world retail. 195-199 - Zhengxing Chen, Truong-Huy D. Nguyen, Yuyu Xu, Christopher Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr:
The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena games. 200-208
Towards recsys that care
- Yiu-Kai Ng, Maria Soledad Pera:
Recommending social-interactive games for adults with autism spectrum disorders (ASD). 209-213 - Sabina Tomkins, Steven Isley, Ben London, Lise Getoor:
Sustainability at scale: towards bridging the intention-behavior gap with sustainable recommendations. 214-218 - Iván Cantador, María E. Cortés-Cediel, Miriam Fernández, Harith Alani:
What's going on in my city?: recommender systems and electronic participatory budgeting. 219-223 - Allison June-Barlow Chaney, Brandon M. Stewart, Barbara E. Engelhardt:
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. 224-232 - Javier Sanz-Cruzado, Pablo Castells:
Enhancing structural diversity in social networks by recommending weak ties. 233-241 - Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver:
Exploring author gender in book rating and recommendation. 242-250
Does it work? metrics and evaluation
- Rohan Tondulkar, Manisha Dubey, Maunendra Sankar Desarkar:
Get me the best: predicting best answerers in community question answering sites. 251-259 - Daniel Valcarce, Alejandro Bellogín, Javier Parapar, Pablo Castells:
On the robustness and discriminative power of information retrieval metrics for top-N recommendation. 260-268 - Michael Jugovac, Dietmar Jannach, Mozhgan Karimi:
Streamingrec: a framework for benchmarking stream-based news recommenders. 269-273 - Yifan Zhong, Tahir Lazaro Sousa Menezes, Vikas Kumar, Qian Zhao, F. Maxwell Harper:
A field study of related video recommendations: newest, most similar, or most relevant? 274-278 - Longqi Yang, Yin Cui, Yuan Xuan, Chenyang Wang, Serge J. Belongie, Deborah Estrin:
Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. 279-287 - Yuan Yao, F. Maxwell Harper:
Judging similarity: a user-centric study of related item recommendations. 288-296
Beyond users and items
- Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, Chi Xu:
Recurrent knowledge graph embedding for effective recommendation. 297-305 - Laura Burbach, Johannes Nakayama, Nils Plettenberg, Martina Ziefle, André Calero Valdez:
User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms. 306-310 - Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, Philip S. Yu:
Spectral collaborative filtering. 311-319 - Qian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Alex Beutel, Francois Belletti, Ed H. Chi:
Categorical-attributes-based item classification for recommender systems. 320-328 - Saikishore Kalloori, Francesco Ricci, Rosella Gennari:
Eliciting pairwise preferences in recommender systems. 329-337 - Marie Al-Ghossein, Pierre-Alexandre Murena, Talel Abdessalem, Anthony Barré, Antoine Cornuéjols:
Adaptive collaborative topic modeling for online recommendation. 338-346
Short papers with poster presentation
- Steven L. Rohall, Margaret Pancost-Heidebrecht, Bill Shirley, Douglas Bacon, Michael A. Tarselli:
Recommendations for chemists: a case study. 347-351 - Hugo Caselles-Dupré, Florian Lesaint, Jimena Royo-Letelier:
Word2vec applied to recommendation: hyperparameters matter. 352-356 - Tiago Cunha, Carlos Soares, André C. P. L. F. de Carvalho:
CF4CF: recommending collaborative filtering algorithms using collaborative filtering. 357-361 - Anita Khadka, Petr Knoth:
Using citation-context to reduce topic drifting on pure citation-based recommendation. 362-366 - Pablo Sánchez, Alejandro Bellogín:
Measuring anti-relevance: a study on when recommendation algorithms produce bad suggestions. 367-371 - Homanga Bharadhwaj, Homin Park, Brian Y. Lim:
RecGAN: recurrent generative adversarial networks for recommendation systems. 372-376 - Mohammad Rostami, David J. Huber, Tsai-Ching Lu:
A crowdsourcing triage algorithm for geopolitical event forecasting. 377-381 - Robin M. E. Swezey, Bruno Charron:
Large-scale recommendation for portfolio optimization. 382-386 - Simen Eide, Ning Zhou:
Deep neural network marketplace recommenders in online experiments. 387-391 - Romain Guigourès, Yuen King Ho, Evgenii Koriagin, Abdul-Saboor Sheikh, Urs Bergmann, Reza Shirvany:
A hierarchical bayesian model for size recommendation in fashion. 392-396 - Yitong Meng, Guangyong Chen, Jiajin Li, Shengyu Zhang:
Psrec: social recommendation with pseudo ratings. 397-401 - David Massimo, Francesco Ricci:
Harnessing a generalised user behaviour model for next-POI recommendation. 402-406 - Wang-Cheng Kang, Julian J. McAuley:
Learning consumer and producer embeddings for user-generated content recommendation. 407-411 - Weiwen Liu, Ruiming Tang, Jiajin Li, Jinkai Yu, Huifeng Guo, Xiuqiang He, Shengyu Zhang:
Field-aware probabilistic embedding neural network for CTR prediction. 412-416 - Noveen Sachdeva, Kartik Gupta, Vikram Pudi:
Attentive neural architecture incorporating song features for music recommendation. 417-421 - Rishabh Misra, Mengting Wan, Julian J. McAuley:
Decomposing fit semantics for product size recommendation in metric spaces. 422-426 - Sumit Sidana, Charlotte Laclau, Massih-Reza Amini:
Learning to recommend diverse items over implicit feedback on PANDOR. 427-431 - Diane Hu, Raphael Louca, Liangjie Hong, Julian J. McAuley:
Learning within-session budgets from browsing trajectories. 432-436 - Bithika Pal, Mamata Jenamani:
Kernelized probabilistic matrix factorization for collaborative filtering: exploiting projected user and item graph. 437-440 - Imen Akermi, Mohand Boughanem, Rim Faiz:
A probabilistic model for intrusive recommendation assessment. 441-445 - Tomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex:
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence. 446-450 - Guy Hadash, Oren Sar Shalom, Rita Osadchy:
Rank and rate: multi-task learning for recommender systems. 451-454 - Yashar Deldjoo, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi:
Audio-visual encoding of multimedia content for enhancing movie recommendations. 455-459 - Xiuyuan Lu, Zheng Wen, Branislav Kveton:
Efficient online recommendation via low-rank ensemble sampling. 460-464 - Hebatallah A. Mohamed Hassan, Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli:
Semantic-based tag recommendation in scientific bookmarking systems. 465-469 - Ramesh Baral, S. S. Iyengar, Tao Li, N. Balakrishnan:
CLoSe: Contextualized Location Sequence Recommender. 470-474 - Dharahas Tallapally, Rama Syamala Sreepada, Bidyut Kr. Patra, Korra Sathya Babu:
User preference learning in multi-criteria recommendations using stacked auto encoders. 475-479
Industry talks: Core algorithms
- Keld T. Lundgaard:
Variational learning to rank (VL2R). 480 - Even Oldridge:
Adapting session based recommendation for features through transfer learning. 481 - Xiaoran Xu, Laming Chen, Songpeng Zu, Hanning Zhou:
Hulu video recommendation: from relevance to reasoning. 482 - Jenny Liu:
Hybrid search: incorporating contextual signals in recommendations at pinterest. 483 - Arnab Bhadury, Aanchan Mohan:
Learning content and usage factors simultaneously to reduce clickbaits. 484
Industry talks: System considerations
- Lina Weichbrodt:
Measuring operational quality of recommendations: industry talk abstract. 485 - Renaud Bourassa:
Building recommender systems with strict privacy boundaries. 486 - Fernando Amat Gil, Ashok Chandrashekar, Tony Jebara, Justin Basilico:
Artwork personalization at netflix. 487-488 - Shahin Sefati, Parsa Saadatpanah, Hassan Sayyadi, Jan Neumann:
Conversational content discovery via comcast X1 voice interface. 489 - Ido Guy:
Connecting sellers and buyers on the world's largest inventory. 490-491
Demonstrations
- Qinghai Zhou, Liangyue Li, Nan Cao, Norbou Buchler, Hanghang Tong:
Extra: explaining team recommendation in networks. 492-493 - Arthur F. Da Costa, Eduardo P. Fressato, Fernando Soares de Aguiar Neto, Marcelo G. Manzato, Ricardo J. G. B. Campello:
Case recommender: a flexible and extensible python framework for recommender systems. 494-495 - Daniel Herzog, Christopher Laß, Wolfgang Wörndl:
Tourrec: a tourist trip recommender system for individuals and groups. 496-497 - Nina Hagemann, Michael P. O'Mahony, Barry Smyth:
Module advisor: a hybrid recommender system for elective module exploration. 498-499 - Masoud Mansoury, Robin Burke, Aldo Ordonez-Gauger, Xavier Sepulveda:
Automating recommender systems experimentation with librec-auto. 500-501 - Takuya Kitazawa, Makoto Yui:
Query-based simple and scalable recommender systems with apache hivemall. 502-503 - Iván García, Alejandro Bellogín:
Towards an open, collaborative REST API for recommender systems. 504-505 - Maximilian Nocker, Gabriele Sottocornola, Markus Zanker, Sanja Baric, Greice Amaral Carneiro, Fabio Stella:
Picture-based navigation for diagnosing post-harvest diseases of apple. 506-507 - Anuradha Bhamidipaty, Daniel M. Gruen, Justin Platz, John Vergo:
Cognitive company discovery. 508-509
Workshops, challenge and late-breaking results
- Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Casper Petersen:
2nd workshop on recommendation in complex scenarios (complexrec 2018). 510-511 - Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Bracha Shapira, Domonkos Tikk, Flavian Vasile, Sander Dieleman:
DLRS 2018: third workshop on deep learning for recommender systems. 512-513 - Thorsten Joachims, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile:
REVEAL 2018: offline evaluation for recommender systems. 514-515 - Toshihiro Kamishima, Pierre-Nicolas Schwab, Michael D. Ekstrand:
2nd FATREC workshop: responsible recommendation. 516 - David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schäfer, Helma Torkamaan, Christoph Trattner:
Third international workshop on health recommender systems (healthrecsys 2018). 517-518 - Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen:
Recsys'18 joint workshop on interfaces and human decision making for recommender systems. 519-520 - Vito Walter Anelli, Pierpaolo Basile, Derek G. Bridge, Tommaso Di Noia, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Markus Zanker:
Knowledge-aware and conversational recommender systems. 521-522 - Shaghayegh (Sherry) Sahebi, Yong Zheng, Weike Pan, Ignacio Fernández:
The 2nd workshop on intelligent recommender systems by knowledge transfer & learning (recsysKTL). 523-524 - Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Markus Zanker:
ACM recsys workshop on recommenders in tourism (rectour 2018). 525-526 - Ching-Wei Chen, Paul Lamere, Markus Schedl, Hamed Zamani:
Recsys challenge 2018: automatic music playlist continuation. 527-528 - Christoph Trattner, Vanessa Murdock, Shuo Chang:
ACM recsys'18 late-breaking results (posters). 529-530
Tutorials
- Omprakash Sonie, Sudeshna Sarkar, Surender Kumar:
Concept to code: learning distributed representation of heterogeneous sources for recommendation. 531-532 - Longqi Yang, Eugene Bagdasaryan, Hongyi Wen:
Modularizing deep neural network-inspired recommendation algorithms. 533-534 - Marko Tkalcic:
Emotions and personality in recommender systems: tutorial. 535-536 - Yashar Deldjoo, Markus Schedl, Balázs Hidasi, Peter Knees:
Multimedia recommender systems. 537-538 - Massimo Quadrana, Paolo Cremonesi:
Sequence-aware recommendation. 539-540 - Jean Garcia-Gathright, Christine Hosey, Brian St. Thomas, Ben Carterette, Fernando Diaz:
Mixed methods for evaluating user satisfaction. 541-542
Doctoral symposium
- Daricia Wilkinson:
Testing a recommender system for self-actualization. 543-547 - Manel Slokom:
Comparing recommender systems using synthetic data. 548-552 - Zhe Li:
Towards the next generation of multi-criteria recommender systems. 553-557 - Giorgia Di Tommaso:
SeRenA: a semantic recommender for all. 558-562 - Kittipitch Kuptavanich:
Using textual summaries to describe a set of products. 563-567 - Qing Ping:
Video recommendation using crowdsourced time-sync comments. 568-572 - Lijie Guo:
Beyond the top-N: algorithms that generate recommendations for self-actualization. 573-577 - Gabriel de Souza Pereira Moreira:
CHAMELEON: a deep learning meta-architecture for news recommender systems. 578-583
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