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RecSys 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
Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization. 51-58 - Dawen Liang, Jaan Altosaar
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
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
Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques. 143-146 - Amra Delic
Observing Group Decision Making Processes. 147-150 - Cataldo Musto
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
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
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
Convolutional Matrix Factorization for Document Context-Aware Recommendation. 233-240 - Balázs Hidasi
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. 241-248
Paper Session 9: Contextual Challenges
- Roberto Pagano, Paolo Cremonesi
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
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
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
T-RecS: A Framework for a Temporal Semantic Analysis of the ACM Recommender Systems Conference. 405-406
Workshops and Challenge
- Marko Tkalcic
4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE). 407 - David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schäfer
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
RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems. 413-414 - Alexandros Karatzoglou, Balázs Hidasi
RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS). 415-416 - Daniel R. Fesenmaier
RecTour 2016: Workshop on Recommenders in Tourism. 417-418 - Toine Bogers, Marijn Koolen
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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