Fast Item Ranking under Neural Network based Measures
Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li
Abstract
Recently, plenty of neural network based recommendation models have demonstrated their strength in modeling complicated relationships between heterogeneous objects (i.e., users and items). However, the applications of these fine trained recommendation models are limited to the off-line manner or the re-ranking procedure (on a pre-filtered small subset of items), due to their time-consuming computations. Fast item ranking under learned neural network based ranking measures is largely still an open question.
Topics & Concepts
Ranking (information retrieval)Computer scienceArtificial neural networkArtificial intelligenceMachine learningComputationData miningInformation retrievalAlgorithmRecommender Systems and TechniquesAdvanced Image and Video Retrieval TechniquesData Management and Algorithms