DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning
In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of "click-consumption". Therefore, in order to improve the success rate of recommendation and reduce computational costs, researchers are trying to model multi-task learning. At present, existing multi-task learning models generally adopt hard parameter sharing or soft parameter sharing architecture, but these two architectures each have certain problems. Therefore, in this work, we propose a novel recommendation model based on real recommendation scenarios, Deep Cross network based on RNN for partial parameter sharing (DCRNN). The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Task LearningRecommendation SystemsSimilar Papers 제목 키워드 기반
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information
Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational c…
EEGElectroencephalogram (EEG)Fault DetectionSeizure Detection+2Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a chall…
graph partitioningManagementA Dynamically Controlled Recurrent Neural Network for Modeling Dynamical Systems
This work proposes a novel neural network architecture, called the Dynamically Controlled Recurrent Neural Network (DCRNN), specifically designed to model dynamical systems that are governed by ordinary differential equa…
State Space ModelsDeep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty,…
Bayesian OptimizationHyperparameter OptimizationUncertainty QuantificationTransfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting
Highway traffic modeling and forecasting approaches are critical for intelligent transportation systems. Recently, deep-learning-based traffic forecasting methods have emerged as state of the art for a wide range of traf…
Graph Neural NetworkTime Series AnalysisTransfer Learning