DEEP REINFORCEMENT LEARNING FOR SKILL RECOMMENDATION

DEEP REINFORCEMENT LEARNING FOR SKILL RECOMMENDATION

Techniques for using deep reinforcement learning for training a recommendation model for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training a recommendation model using deep reinforcement learning and a Markov decision process, where the Mar...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: YAN, Xiao, YANG, Jaewon, WANG, Yiming, He, Qidu, LI, Yanen, Niu, Sufeng, Zheng, Chujie
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Techniques for using deep reinforcement learning for training a recommendation model for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training a recommendation model using deep reinforcement learning and a Markov decision process, where the Markov decision process has a state space including state embeddings of a plurality of reference users, an action space including action embeddings of the plurality of reference users, and a reward function. The reward function may be configured to issue a first reward based on current impression interaction data and a second reward based on a measurement of engagement of the reference user with the online service.