Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale
Barrie Kersbergen, Olivier Sprangers, Sebastian Schelter
Abstract
Session-based recommendation predicts the next item with which a user will interact, given a sequence of her past interactions with other items. This machine learning problem targets a core scenario in e-commerce platforms, which aim to recommend interesting items to buy to users browsing the site. Session-based recommenders are difficult to scale due to their exponentially large input space of potential sessions. This impedes offline precomputation of the recommendations, and implies the necessity to maintain state during the online computation of next-item recommendations.
Topics & Concepts
PrecomputationSession (web analytics)Computer scienceRecommender systemLatency (audio)Scale (ratio)Low latency (capital markets)E-commerceComputationMultimediaWorld Wide WebComputer networkAlgorithmTelecommunicationsQuantum mechanicsPhysicsRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchTopic Modeling