Loading patent…
AssigneesBaidu USA LLC, Baidu.Com Times Technology (Beijing) Co., Ltd.
InventorsHongliang FEI, Jingyuan ZHANG, Xingxuan ZHOU, Junhao ZHAO, Banghu YIN, Ping LI
Estimating the term…
Deep neural network (DNN) models have been widely used for user-relevance content prediction. Presented herein is a new user-relevance framework, embodiments of which may be referred as Gating-Enhanced Multi-task Neural Networks (GemNN). In one or more, neural network-based multi-task learning model embodiments herein predict user engagement with content in a coarse-to-fine manner, which gradually reduces content candidates and allows parameter sharing from upstream tasks to downstream tasks to improve the training efficiency. Also, in one or more embodiments, a gating mechanism was introduced between embedding layers and multi-layer perceptions to learn feature interactions and control the information flow fed to MLP layers. Tested embodiments demonstrated considerable improvements over prior approaches.
A computer-implemented method for delivering content to a user, the method comprising:
A system comprising:
A non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by at least one processor, causes steps to be performed comprising:
Dependencies are inferred from the claim text.