Program

9:00-9:10 Opening Remarks

9:10-10:40 Case Studies I

9:10-9:40 Jordan Silva, Rodrygo L. T. Santos, Nivio Ziviani: Universidade Federal de Minas Gerais. Nearby places: on location-based pruning for point-of-interest recommendation [slides]

9:40-10:10 Kristiana Schneck: Pandora. Personalized Concert Recommendations at Pandora

10:10-10:40 Sergio Oramas: UPF, Spain. Knowledge Extraction and Feature Learning for Music Recommendation in the Long Tail [slides]

10:40-11:00 Coffee Break

11:00-12:30 Algorithms

11:00-11:30 Nicolás Torres, Marcelo Mendoza: Universidad Técnica Federico Santa María, Chile. Clustering approaches for Top-N Recommender Systems [pdf] [slides]

11:30-12:00 Laming Chen, Guoxin Zhang, Hanning Zhou: Hulu. Improving the Diversity of Top-N Recommendation via Determinantal Point Process [pdf] [slides]

16:00-16:30 Maciej Kula: Ravelin. Binary Latent Representations for Efficient Ranking: Empirical Evaluation [pdf] [slides]

12:30-14:00 Lunch

14:00-15:30 Case Studies II

14:00-14:30 Sandra Garcia: Schibsted Media Group. Building search and discovery services for marketplaces and media houses [slides]

14:30-15:00 Noam Koenigsten: Microsoft. Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation [slides]

15:00-15:30 Humberto Corona: Zalando. Understanding customer intent at scale in an e-commerce platform (contact the speaker for the slides)

15:30-15:40 Closing remarks

15:40-16:00 Coffee break

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