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Oct 13th, 2013 Hongkong, China
9:00am – 9:15am Opening remarks
Organizers: Tao Ye, Pandora; Danny Bickson, GraphLab; Quan Yuan, Taobao
9:15am – 10:15am Keynote 1:
- Recommendation at Netflix Scale, Justin Basilico, Netflix
10:15am – 10:30am Break
10:30am – 12:00pm Session 1: Architecture and Systems. Session Chair: Tao Ye
- Cross Device Ad Targeting at Scale – Jerry Ye, Drawbridge.
- Bandits under pressure – Maximiliano Neustadt and Andrei Oghina, TMG. pdf
- Large-scale Recommendations in a Dynamic Marketplace – Jayasimha Katukuri, Rajyashree Mukherjee and Tolga Konik, eBay. pptx
12:00pm – 1:00pm Lunch
1:00pm – 2:20pm Session 2: Scalable Algorithms. Session Chair: Yi Zhang
- Efficient Top-N Recommendation by Linear Regression – Mark Levy and Kris Jack, Mendeley .
- Peacock: Learning 10^5 Latent Topics, Yi Wang. pdf
- Scalable Variational Bayesian Matrix Factorization, Yong-Deok Kim and Seungjin Choi, Pohang University of Science and Technology , S. Korea
2:20pm – 3:40pm Session 3: Application and Evaluation. Session Chair: Quan Yuan
- Recommender SaaS in Practice – Tianjian Chen, Jianbo Zhao and Xin Sun, Baidu. pptx
- Tag Recommendation: Expanding Context with Similar Images – Heath Hohwald and Eliot Brenner, ShutterStock
- A Top-N Recommender System Evaluation Protocol Inspired by Deployed Systems - Alan Said, Alejandro Bellogin and Arjen de Vries, Centrum Wiskunde & Informatica Amsterdam
3:40pm – 4:00pm Break
4:00pm – 4:40pm Keynote 2:
4:40pm – 5:40pm Panel Discussion
- What recommendation problems will you be solving in 3 years? Can current tech stacks keep up naturally?