Workshop on 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.

Organizers
  • Rani Nelken, Outbrain, USA
Website

http://recprofile.org

Date

Thursday, Sept 15, 2016, 14:00-17:30

Location

IBM (Aud B)

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