Baidu USA LLC

Baidu.com Times Technology (beijing) Co. Ltd.

Jingyuan Zhang

Hongliang Fei

Ping Li

Junhao Zhao

Xingxuan Zhou

Banghu Yin

Deep neural network (DNN) models have been widely used for user-relevance content prediction. Presented herein are embodiments of a new user-relevance framework, which may be referred as Gating-Enhanced Multi-task Neural Networks (GemNN) embodiments. 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.

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San Jose, CA, United States of America

Jingyuan Zhang