Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations-Reference-Cited by-同舟云学术

Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations

Published:2020-09-22 Issue: Volume: Page:
ISSN:
Container-title:Fourteenth ACM Conference on Recommender Systems
language:
Short-container-title:

Author:

Tang Hongyan¹,Liu Junning¹,Zhao Ming¹,Gong Xudong¹

Affiliation:

1. Tencent PCG, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3383313.3412236

Reference26 articles.

1. Ask the GRU

2. Co-Attentive Multi-Task Learning for Explainable Recommendation

3. Wide & Deep Learning for Recommender Systems

4. Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml

Cited by 293 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction and optimization of pure electric vehicle tire/road structure-borne noise based on knowledge graph and multi-task ResNet;Expert Systems with Applications;2024-12

2. Machining feature and topological relationship recognition based on a multi-task graph neural network;Advanced Engineering Informatics;2024-10

3. An interpretable two-branch bi-coordinate network based on multi-grained domain knowledge for classification of thyroid nodules in ultrasound images;Medical Image Analysis;2024-10

4. A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation;ACM Transactions on Recommender Systems;2024-09-13

5. A Multi-Task Convolutional Neural Network Relative Radiometric Calibration Based on Temporal Information;Remote Sensing;2024-09-09