Profiling User Preferences for Dynamic Online and Real-Time Recommendations

RecSys'16 RecProfile

Profiling User Preferences for Dynamic Online and Real-Time Recommendations

Constructing user preference profiles is crucial for accurate predictions in recommendation systems. While traditional profiling is well understood, modern dynamic online recommendation services raise interesting challenges for user profiling. First, effectively representing user preferences in dynamic settings requires novel modeling techniques such as latent factorization. Second, for space and time efficiency, it might be infeasible to store all observed user profile signals, which requires resorting to streaming or sketching algorithms. Third, there are significant engineering architecture challenges to efficiently maintain user profiles for large-scale real-time recommendations. We welcome papers addressing both algorithmic and architectural facets of the problem.
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Time: Thursday, September 15th, 14:00-17:30

Session 1

14:00-14:10 Opening introduction

14:10-14:50 Invited Talk: Laurent Charlin (HEC Montréal) Modeling User Preferences

14:50-15:10 Takuya Kitazawa. Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation

15:10-15:30 Jorge Díez, David Martínez-Rego, Amparo Alonso-Betanzos, Oscar Luaces and Antonio Bahamonde. Metrical Representation of Readers and Articles in a Digital Newspaper

15:30-16:00 Coffee Break

Session 2

16:00-16:40 Invited Talk: Jonathan Siddharth (Rover) Building User Interest Profiles in Real-time for a Content Recommender System

16:40-17:00 Megan Bingham-Walker and Richard Searle. Personalized User Profiling for Time-Dependent Recommendation of Structured Products

17:00-17:20 Ido Tamir, Roy Bass, Baruch Brutman, Yoram Dayagi, Guy Kobrinsky and Ronny Lempel. Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences

17:20-17:30 Wrap up