Parallel recurrent neural network architectures for feature-rich session-based recommendations

RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems

Volumn , Issue , 2016, Pages 241-248

  Hidasi, Balázs ^a   Quadrana, Massimo ^b   Karatzoglou, Alexandros ^c   Tikk, Domonkos ^a  

^a Gravity Research and Development Inc   (Hungary)

^b POLITECNICO DI MILANO   (Italy)

^c TELEFONICA RESEARCH   (Spain)

Author keywords

Deep learning; Gated recurrent units; Recommender systems; Recurrent neural networks; Training strategies

Indexed keywords

FACTORIZATION; NETWORK ARCHITECTURE; NEURAL NETWORKS; PARALLEL ARCHITECTURES; RECOMMENDER SYSTEMS;

DATA-SOURCES; DEEP LEARNING; GATED RECURRENT UNITS; MATRIX FACTORIZATIONS; RICH FEATURES; TRAINING STRATEGY; USER PROFILE; USER SESSIONS;

RECURRENT NEURAL NETWORKS;

EID: 84991217793     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2959100.2959167     Document Type: Conference Paper

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