Program

The conference sessions will take place at the Auditorium, Wong Cheung Lo Hui Yuet Hall, AC3, City University of Hong Kong. Workshops, Tutorials and the Doctoral Symposium will take place at AC1 (see the respective detail pages).

Downloads

• Program at a glance
• Official program brochure (as PDF, 10.0MB)

Conference Schedule

Sat, Oct 12
Registration: Blue Zone, Academic Concourse, 4/F, Academic 1 (AC1).

• Workshop Decisions: Human Decision Making in Recommender Systems
• Workshop RepSys: Reproducibility and Replication in RS Evaluation
• Workshop CrowdRec: Crowdsourcing and Human Computation for RS (half day, PM)
• Tutorial Mining Social Networks for Recommendation
• Tutorial Learning to Rank
• Doctoral Symposium

Sun, Oct 13
Registration: Blue Zone, Academic Concourse, 4/F, Academic 1 (AC1).

• Workshop RSWeb: Recommender Systems and the Social Web
• Workshop NRS: News Recommender Systems Workshop and Challange (half day, AM)
• Workshop SeRSy: RS meet Big Data and Semantic Technologies (half day, PM)
• Workshop LargeScale: Large Scale RSs: Research and Best Practice
• Tutorial Beyond Friendship
• Tutorial Preference Handling

Mon, Oct 14
Registration: Foyer, Auditorium – Wong Cheung Lo Hui Yuet Hall, Academic 3 (AC3)

• Keynote: Information Extraction, Sentiment Analysis and Recommendations
• Session Context-Aware (10.45 – 12.30)
  • Context-Aware Review Helpfulness Rating Prediction by J. Tang, H. Gao, X. Hu and H. Liu
  • Query-Driven Context Aware Recommendation by N. Hariri, B. Mobasher and Robin Burke
  • Location-aware Music Recommendation Using Auto-Tagging and Hybrid Matching by M. Kaminskas, F. Ricci and M. Schedl
  • Spatial Topic Modeling in Online Social Media for Location Recommendation by B. Hu and M. Ester
  • Evaluating Top-N Recommendations ‘When the Best are Gone’ by P. Cremonesi, F. Garzotto and M. Quadrana
• Session Methods, Algorithms and Theories (14.00 – 15.45)
  • Orthogonal Query Recommendation by H. Vahabi, M. Ackerman, D. Loker, R. Baeza-Yates and A. Lopez-Ortiz
  • Understanding and Improving Relational Matrix Factorization in Recommender Systems by L. Pu and B. Faltings
  • Retargeted Matrix Factorization for Collaborative Filtering by O. Koyejo, S. Acharyya and J. Ghosh
  • Trading-off Among Accuracy, Similarity, Diversity, and Long Tail: A Graph-based Recommendation Approach by L. Shi
  • Nonlinear Latent Factorization by Embedding Multiple User Interests by J. Weston, R. Weiss and H. Yee
• Session Social Media and RS (16.15 – 18.00)
  • Diffusion-aware Personalized Social Update Recommendation by Y. Pan, K. Chen and Y. Yu
  • Recommending Branded Products from Social Media by Y. Zhang and M. Pennacchiotti
  • Top-N Recommendations from Implicit Feedback leveraging Linked Open Data by V. C. Ostuni, T. Di Noia, E. Di Sciascio and R. Mirizzi
  • Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks by H. Gao, J. Tang, X. Hu and H. Liu
  • The Curated Web: A Recommendation Challenge by Z. Saaya, R. Rafter, M. Schaal and B. Smyth
• Reception: Posters & Demos (18.00 – 21.00, 9/F, Creative Media Centre, City University)

Tue, Oct 15
Registration: Foyer, Auditorium – Wong Cheung Lo Hui Yuet Hall, Academic 3 (AC3).

• Session Media Recommendation (8.30 – 10.15)
  • Personalized News Recommendation with Context Trees by F. Garcin, C. Dimitrakakis and B. Faltings
  • What to Read Next?: Making Personalized Book Recommendations for K-12 Users by M. Pera and Y. Ng
  • Movie Recommender System for Profit Maximization by A. Azaria, A. Hassidim, S. Kraus, A. Eshkol, O. Weintraub and I. Netanely
  • Xbox Movies Recommendations: Variational Bayes Matrix Factorization with Embedded Feature Selection by N. Koenigstein and U. Paquet
  • Personalized Next-song Recommendation in Online Karaokes by X. Wu, Q. Liu, E. Chen, J. Lv, C. Cao and G. Hu
• Keynote: Recommendation in Online Advertising
• Industry Session I (10.45 – 12.30)
  • Catch-up TV Recommendations: Show Old Favourites and Find New Ones by M. Xu, S. Berkovsky, S. Ardon, S. Triukose, A. Mahanti and I. Koprinska
  • Generating supplemental content information using virtual profiles by H. Liu, M. Amin, B. Yan and A. Bhasin
• Session User Experience (14.00 – 15.45)
  • Topic Diversity in Tag Recommendation by F. Santos, M. Goncalves and J. Almeida
  • Rating support interfaces to improve user experience and recommender accuracy by T. Nguyen, D. Kluver, T. Wang, P. Hui, M. Ekstrand, M. Willemsen and J. Riedl
  • ReComment: Towards Critiquing-based Recommendation with Speech Interaction by P. Grasch, A. Felfernig and F. Reinfrank
  • Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text by J. McAuley and J. Leskovec
  • Improving Augmented Reality Using Recommender Systems by Z. Zhang, S. Shang, S. Kulkarni and P. Hui
• Session Beyond Ratings (16.15 – 18.00)
  • Exploiting non-content taste attributes through hybrid recommendation method by F. Rocha, J. Konstan and W. Meira Jr.
  • Hybrid Event Recommendation using Linked Data and User Diversity by H. Khrouf and R. Troncy
  • Pairwise Feedback in Recommendation: Experiments with Community Recommendation on LinkedIn by A. Sharma and B. Yan
  • Which app will you use next? Collaborative Filtering with Interactional Context by N. Natarajan, D. Shin and I. Dhillon
  • A Food Recommender for Patients in a Care Facility by T. De Pessemier, S. Dooms and L. Martens
• Awards Banquet (18.00 – 22.00, House of Canton, LG2-40, Festival Walk, Kowloon Tong)

Wed, Oct 16
Registration: Foyer, Auditorium – Wong Cheung Lo Hui Yuet Hall, Academic 3 (AC3).

• Industry Session II (8.30 – 10.15)
  • Presentations by A. Broder (Google, USA), Q. Yuan (Taobao, China), N. Li (Tencent, China), W. Y. Dai (Huawei, China) and I. Guy (IBM Research, Israel)
• Keynote: Recommendation for Happiness
• RecSys Challenge: Results and Winners
• Session Methods, Algorithms and Theories II (14.00 – 15.45)
  • Rating-Prediction and Ranking by H. Steck
  • You Are What You Consume: A Bayesian Method For Personalized Recommendations by K. Babas, G. Chalkiadakis and E. Tripolitakis
  • To Personalize or Not: A Risk Management Perspective by W. Zhang, J. Wang, B. Chen, X. Zhao
  • Online Multi-Task Collaborative Filtering for On-the-Fly Recommender Systems by J. Wang, S. Hoi and P. Zhao
  • Learning to Rank Recommendations with the k-Order Statistic Loss by J. Weston, H. Yee and R. Weiss
• Session Scalability (16.15 – 18.00)
  • A Fast Parallel SGD for Matrix Factorization in Shared Memory Systems by Y. Zhuang, W. Chin, Y. Juan and C. Lin (Winner of the Best Paper Award)
  • DrunkardMob: Billions of Random Walks on Just a PC by A. Kyrola
  • Using Maximum Coverage to Optimize Recommendation Systems in E-Commerce by M. Hammar, R. Karlsson and B. Nilsson
  • Efficient Top-N Recommendation for Very Large Scale Binary Rated Datasets by F. Aiolli
  • Distributed Matrix Factorization with MapReduce using a series of Broadcast-Joins by S. Schelter, C. Boden, M. Schenck, A. Alexandrov and V. Markl