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ORCIDRecSys 2016: Boston, MA, USA
- Shilad Sen, Werner Geyer, Jill Freyne, Pablo Castells:
Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016. ACM 2016, ISBN 978-1-4503-4035-9
Invited Keynotes
- Claudia Perlich:
Automated Machine Learning in the Wild. 1 - Shashi Thakur:
Personalization for Google Now: User Understanding and Application to Information Recommendation and Exploration. 3
Paper Session 1: Beyond Accuracy
- Dietmar Jannach, Gediminas Adomavicius:
Recommendations with a Purpose. 7-10 - Bart P. Knijnenburg, Saadhika Sivakumar, Daricia Wilkinson:
Recommender Systems for Self-Actualization. 11-14 - Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet
:
A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. 15-22 - Zhen Qin, Ish Rishabh, John Carnahan:
A Scalable Approach for Periodical Personalized Recommendations. 23-26 - Matthew Mitsui, Chirag Shah:
Multi-Word Generative Query Recommendation Using Topic Modeling. 27-30 - Marco Rossetti, Fabio Stella, Markus Zanker:
Contrasting Offline and Online Results when Evaluating Recommendation Algorithms. 31-34 - Choon Hui Teo, Houssam Nassif, Daniel N. Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, S. V. N. Vishwanathan:
Adaptive, Personalized Diversity for Visual Discovery. 35-38 - Jacek Wasilewski, Neil Hurley:
Intent-Aware Diversification Using a Constrained PLSA. 39-42
Paper Session 2: Algorithms I
- Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin:
Field-aware Factorization Machines for CTR Prediction. 43-50 - Jie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang:
Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization. 51-58 - Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei:
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. 59-66 - Evangelia Christakopoulou, George Karypis:
Local Item-Item Models For Top-N Recommendation. 67-74 - Bikash Joshi, Franck Iutzeler, Massih-Reza Amini:
Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization. 75-78 - Chih-Ming Chen, Ming-Feng Tsai, Yu-Ching Lin, Yi-Hsuan Yang:
Query-based Music Recommendations via Preference Embedding. 79-82
Paper Session 3: Cold Start and Hybrid Methods
- Xuezhi Cao, Yong Yu:
Joint User Modeling across Aligned Heterogeneous Sites. 83-90 - Evgeny Frolov, Ivan V. Oseledets:
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks. 91-98 - Sujoy Roy, Sharath Chandra Guntuku:
Latent Factor Representations for Cold-Start Video Recommendation. 99-106 - Trapit Bansal, David Belanger, Andrew McCallum:
Ask the GRU: Multi-task Learning for Deep Text Recommendations. 107-114 - Szu-Yu Chou, Yi-Hsuan Yang, Jyh-Shing Roger Jang, Yu-Ching Lin:
Addressing Cold Start for Next-song Recommendation. 115-118 - Ignacio Fernández-Tobías, Paolo Tomeo, Iván Cantador, Tommaso Di Noia, Eugenio Di Sciascio:
Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback. 119-122
Paper Session 4: User in the Loop
- André Calero Valdez, Martina Ziefle, Katrien Verbert:
HCI for Recommender Systems: the Past, the Present and the Future. 123-126 - Patrick Shafto, Olfa Nasraoui:
Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive Models. 127-130 - Qian Zhao, Shuo Chang, F. Maxwell Harper, Joseph A. Konstan:
Gaze Prediction for Recommender Systems. 131-138 - Raghav Pavan Karumur, Tien T. Nguyen, Joseph A. Konstan:
Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens. 139-142 - Saikishore Kalloori, Francesco Ricci, Marko Tkalcic:
Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques. 143-146 - Amra Delic, Julia Neidhardt, Thuy Ngoc Nguyen, Francesco Ricci, Laurens Rook, Hannes Werthner, Markus Zanker:
Observing Group Decision Making Processes. 147-150 - Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro:
ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud. 151-154 - Georgios Askalidis, Edward C. Malthouse:
The Value of Online Customer Reviews. 155-158
Paper Session 5: Trust and Reliability
- Qingpeng Cai, Aris Filos-Ratsikas, Chang Liu, Pingzhong Tang:
Mechanism Design for Personalized Recommender Systems. 159-166 - Jermaine Marshall, Dong Wang:
Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing. 167-174 - Shuo Chang, F. Maxwell Harper, Loren Gilbert Terveen:
Crowd-Based Personalized Natural Language Explanations for Recommendations. 175-182
Paper Session 6: Applications
- Asmaa Elbadrawy, George Karypis:
Domain-Aware Grade Prediction and Top-n Course Recommendation. 183-190 - Paul Covington, Jay Adams, Emre Sargin:
Deep Neural Networks for YouTube Recommendations. 191-198 - Yuri M. Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel A. Galron, Ryan Snyder, Paul Wang:
Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion. 199-202 - Idir Benouaret, Dominique Lenne:
A Package Recommendation Framework for Trip Planning Activities. 203-206
Paper Session 7: Past, Present & Future
- Amos Azaria, Jason I. Hong:
Recommender Systems with Personality. 207-210 - Xavier Amatriain, Justin Basilico:
Past, Present, and Future of Recommender Systems: An Industry Perspective. 211-214 - Tamas Motajcsek, Jean-Yves Le Moine, Martha A. Larson, Daniel Kohlsdorf, Andreas Lommatzsch, Domonkos Tikk, Omar Alonso, Paolo Cremonesi, Andrew M. Demetriou, Kristaps Dobrajs, Franca Garzotto, Ayse Göker, Frank Hopfgartner, Davide Malagoli, Thuy Ngoc Nguyen, Jasminko Novak, Francesco Ricci, Mario Scriminaci, Marko Tkalcic, Anna Zacchi:
Algorithms Aside: Recommendation As The Lens Of Life. 215-219 - Michael D. Ekstrand, Martijn C. Willemsen:
Behaviorism is Not Enough: Better Recommendations through Listening to Users. 221-224
Paper Session 8: Deep Learning
- Flavian Vasile, Elena Smirnova, Alexis Conneau:
Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation. 225-232 - Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Yu:
Convolutional Matrix Factorization for Document Context-Aware Recommendation. 233-240 - Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, Domonkos Tikk:
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. 241-248
Paper Session 9: Contextual Challenges
- Roberto Pagano, Paolo Cremonesi, Martha A. Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana:
The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems. 249-252 - Haokai Lu, James Caverlee, Wei Niu:
Discovering What You're Known For: A Contextual Poisson Factorization Approach. 253-260 - Hancheng Ge, James Caverlee, Haokai Lu:
TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation. 261-268 - Yiming Liu, Xuezhi Cao, Yong Yu:
Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling. 269-272 - Bartlomiej Twardowski:
Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks. 273-276 - Denis Vuckovac, Julia Wamsler, Alexander Ilic, Martin Natter:
Getting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender Systems. 277-280 - Ramesh Baral, Tao Li:
MAPS: A Multi Aspect Personalized POI Recommender System. 281-284
Paper Session 10: Social Perspective
- Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca:
Recommending New Items to Ephemeral Groups Using Contextual User Influence. 285-292 - Roy Levin, Hassan Abassi, Uzi Cohen:
Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks. 293-300 - Peixin Gao, Hui Miao, John S. Baras, Jennifer Golbeck:
STAR: Semiring Trust Inference for Trust-Aware Social Recommenders. 301-308 - Ruining He, Chen Fang, Zhaowen Wang, Julian J. McAuley:
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation. 309-316 - Trong T. Nguyen, Hady Wirawan Lauw:
Representation Learning for Homophilic Preferences. 317-324
Paper Session 11: Algorithms II
- Rose Catherine, William W. Cohen:
Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach. 325-332 - Ramon Lopes, Renato M. Assunção, Rodrygo L. T. Santos:
Efficient Bayesian Methods for Graph-based Recommendation. 333-340 - Chao-Yuan Wu, Christopher V. Alvino, Alexander J. Smola, Justin Basilico:
Using Navigation to Improve Recommendations in Real-Time. 341-348 - Mike Gartrell, Ulrich Paquet, Noam Koenigstein:
Bayesian Low-Rank Determinantal Point Processes. 349-356 - Suvodip Dey, Pabitra Mitra, Kratika Gupta:
Recommending Repeat Purchases using Product Segment Statistics. 357-360 - Babak Loni, Roberto Pagano, Martha A. Larson, Alan Hanjalic:
Bayesian Personalized Ranking with Multi-Channel User Feedback. 361-364
Industry Session 1
- Saúl Vargas, Maya Hristakeva, Kris Jack:
Mendeley: Recommendations for Researchers. 365 - Evan Estola:
When Recommendation Systems Go Bad. 367 - Dhaval Shah, Pramod Koneru, Parth Shah, Rohit Parimi:
News Recommendations at scale at Bloomberg Media: Challenges and Approaches. 369 - Max Sklar:
Marsbot: Building a Personal Assistant. 371 - Kurt Jacobson, Vidhya Murali, Edward Newett, Brian Whitman, Romain Yon:
Music Personalization at Spotify. 373
Industry Session 2
- Justin Basilico, Yves Raimond:
Recommending for the World. 375 - Òscar Celma:
The Exploit-Explore Dilemma in Music Recommendation. 377 - Katherine A. Livins:
Feature Selection For Human Recommenders. 379 - Jan Krasnodebski, John Dines:
Considering Supplier Relations and Monetization in Designing Recommendation Systems. 381-382 - Denise Ichinco, Sahil Zubair, Jana Eggers, Nathan Wilson:
A Cross-Industry Machine Learning Framework with Explicit Representations. 383
Industry Session 3
- Adam Anthony, Yu-Keng Shih, Ruoming Jin, Yang Xiang:
Leveraging a Graph-Powered, Real-Time Recommendation Engine to Create Rapid Business Value. 385-386 - Roman Zykov:
Hypothesis Testing: How to Eliminate Ideas as Soon as Possible. 387 - Lei Yang, Xavier Amatriain:
Recommending the World's Knowledge: Application of Recommender Systems at Quora. 389 - Levent Koc, Cyrus Master:
Multi-corpus Personalized Recommendations on Google Play. 391 - Stephanie Kaye Rogers:
Item-to-item Recommendations at Pinterest. 393
Demonstrations
- Gabriel de Souza Pereira Moreira, Gilmar Alves de Souza:
A Recommender System to tackle Enterprise Collaboration. 395-396 - Yueming Sun, Yi Zhang, Yunfei Chen, Roger Jin:
Conversational Recommendation System with Unsupervised Learning. 397-398 - Ido Tamir, Roy Bass, Guy Kobrinsky, Baruch Brutman, Ronny Lempel, Yoram Dayagi:
Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences. 399-400 - Jiawei Hu, Zhiqiang Zhang, Jian Liu, Chuan Shi, Philip S. Yu, Bai Wang:
RecExp: A Semantic Recommender System with Explanation Based on Heterogeneous Information Network. 401-402 - Christian Rakow, Andreas Lommatzsch, Till Plumbaum:
Topical Semantic Recommendations for Auteur Films. 403-404 - Fedelucio Narducci, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro:
T-RecS: A Framework for a Temporal Semantic Analysis of the ACM Recommender Systems Conference. 405-406
Workshops and Challenge
- Marko Tkalcic, Berardina De Carolis, Marco de Gemmis, Andrej Kosir:
4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE). 407 - David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schäfer, Christoph Trattner:
Engendering Health with Recommender Systems. 409-410 - Rani Nelken:
RecProfile '16: Workshop on Profiling User Preferences for Dynamic, Online, and Real-Time recommendations. 411-412 - Peter Brusilovsky, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Nava Tintarev, Martijn C. Willemsen:
RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems. 413-414 - Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Oren Sar Shalom, Haggai Roitman, Bracha Shapira, Lior Rokach:
RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS). 415-416 - Daniel R. Fesenmaier, Tsvi Kuflik, Julia Neidhardt:
RecTour 2016: Workshop on Recommenders in Tourism. 417-418 - Toine Bogers, Marijn Koolen, Cataldo Musto, Pasquale Lops, Giovanni Semeraro:
Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016). 419-420 - Tao Ye, Danny Bickson, Denis Parra:
LSRS'16: Workshop on Large-Scale Recommender Systems. 421-422 - Jan Neumann, John Hannon, Claudio Riefolo, Hassan Sayyadi:
3rd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2016). 423-424 - Fabian Abel, András A. Benczúr, Daniel Kohlsdorf, Martha A. Larson, Róbert Pálovics:
RecSys Challenge 2016: Job Recommendations. 425-426
Tutorials
- Ludovico Boratto:
Group Recommender Systems. 427-428 - Panagiotis Symeonidis:
Matrix and Tensor Decomposition in Recommender Systems. 429-430 - Ido Guy, Luiz Augusto Pizzato:
People Recommendation Tutorial. 431-432 - Xavier Amatriain, Deepak Agarwal:
Tutorial: Lessons Learned from Building Real-life Recommender Systems. 433
Doctoral Symposium
- Marko Gasparic:
Context-Based IDE Command Recommender System. 435-438 - Agung Toto Wibowo:
Generating Pseudotransactions for Improving Sparse Matrix Factorization. 439-442 - Abhishek Srivastava:
Gray Sheep, Influential Users, User Modeling and Recommender System Adoption by Startups. 443-446 - Catalin-Mihai Barbu:
Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources. 447-450 - Fatemeh Pourgholamali:
Mining Information for the Cold-Item Problem. 451-454 - Hanna Schäfer:
Personalized Support for Healthy Nutrition Decisions. 455-458 - Adem Sabic:
Proactive Recommendation Delivery. 459-462 - Dimitris Paraschakis:
Recommender Systems from an Industrial and Ethical Perspective. 463-466
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