{"title":"动态上下文加权嵌入:一种新的预测建模方法","authors":"Zhai Yue, Mohd Ridwan Abd Razak, Lufeng Li","doi":"10.1049/cmu2.70036","DOIUrl":null,"url":null,"abstract":"<p>Employee satisfaction prediction models often struggle to capture the complex, non-linear relationships between compensation and job satisfaction, particularly in heterogeneous organisational contexts. This paper introduces a novel deep learning framework incorporating multiple technical innovations to address these challenges. The proposed approach employs a dual-pathway neural architecture with compensation-specific processing modules to explicitly model the non-linear interactions between compensation factors and other job attributes across diverse organisational settings. A differentiated embedding strategy transforms raw features into rich, context-aware representations, enabling the capture of subtle patterns in employee satisfaction dynamics. The framework integrates a context-sensitive attention mechanism that automatically identifies and weighs relevant features based on organisational characteristics and temporal patterns, alongside a specialised loss function that adaptively emphasises difficult-to-predict cases, improving performance on complex satisfaction patterns. This model demonstrates robust performance across diverse industry settings, handling missing data and class imbalance effectively. Extensive comparative experiments against state-of-the-art methods (LSTM-Attention, GNN-based approaches and traditional ML models) across multiple datasets show significant improvements, with prediction accuracy increasing by 5.2%–8.5%, mean squared error decreasing by 10.3%–15.2% and AUC-ROC metrics improving by 7.8%. Further analysis reveals superior performance in handling temporal dependencies and organisational context variations, with particular strength in predicting satisfaction levels during significant organisational changes.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70036","citationCount":"0","resultStr":"{\"title\":\"Dynamic Context-Weighted Embeddings: A Novel Approach to Predictive Modelling\",\"authors\":\"Zhai Yue, Mohd Ridwan Abd Razak, Lufeng Li\",\"doi\":\"10.1049/cmu2.70036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Employee satisfaction prediction models often struggle to capture the complex, non-linear relationships between compensation and job satisfaction, particularly in heterogeneous organisational contexts. This paper introduces a novel deep learning framework incorporating multiple technical innovations to address these challenges. The proposed approach employs a dual-pathway neural architecture with compensation-specific processing modules to explicitly model the non-linear interactions between compensation factors and other job attributes across diverse organisational settings. A differentiated embedding strategy transforms raw features into rich, context-aware representations, enabling the capture of subtle patterns in employee satisfaction dynamics. The framework integrates a context-sensitive attention mechanism that automatically identifies and weighs relevant features based on organisational characteristics and temporal patterns, alongside a specialised loss function that adaptively emphasises difficult-to-predict cases, improving performance on complex satisfaction patterns. This model demonstrates robust performance across diverse industry settings, handling missing data and class imbalance effectively. Extensive comparative experiments against state-of-the-art methods (LSTM-Attention, GNN-based approaches and traditional ML models) across multiple datasets show significant improvements, with prediction accuracy increasing by 5.2%–8.5%, mean squared error decreasing by 10.3%–15.2% and AUC-ROC metrics improving by 7.8%. Further analysis reveals superior performance in handling temporal dependencies and organisational context variations, with particular strength in predicting satisfaction levels during significant organisational changes.</p>\",\"PeriodicalId\":55001,\"journal\":{\"name\":\"IET Communications\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70036\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/cmu2.70036\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Communications","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cmu2.70036","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Dynamic Context-Weighted Embeddings: A Novel Approach to Predictive Modelling
Employee satisfaction prediction models often struggle to capture the complex, non-linear relationships between compensation and job satisfaction, particularly in heterogeneous organisational contexts. This paper introduces a novel deep learning framework incorporating multiple technical innovations to address these challenges. The proposed approach employs a dual-pathway neural architecture with compensation-specific processing modules to explicitly model the non-linear interactions between compensation factors and other job attributes across diverse organisational settings. A differentiated embedding strategy transforms raw features into rich, context-aware representations, enabling the capture of subtle patterns in employee satisfaction dynamics. The framework integrates a context-sensitive attention mechanism that automatically identifies and weighs relevant features based on organisational characteristics and temporal patterns, alongside a specialised loss function that adaptively emphasises difficult-to-predict cases, improving performance on complex satisfaction patterns. This model demonstrates robust performance across diverse industry settings, handling missing data and class imbalance effectively. Extensive comparative experiments against state-of-the-art methods (LSTM-Attention, GNN-based approaches and traditional ML models) across multiple datasets show significant improvements, with prediction accuracy increasing by 5.2%–8.5%, mean squared error decreasing by 10.3%–15.2% and AUC-ROC metrics improving by 7.8%. Further analysis reveals superior performance in handling temporal dependencies and organisational context variations, with particular strength in predicting satisfaction levels during significant organisational changes.
期刊介绍:
IET Communications covers the fundamental and generic research for a better understanding of communication technologies to harness the signals for better performing communication systems using various wired and/or wireless media. This Journal is particularly interested in research papers reporting novel solutions to the dominating problems of noise, interference, timing and errors for reduction systems deficiencies such as wasting scarce resources such as spectra, energy and bandwidth.
Topics include, but are not limited to:
Coding and Communication Theory;
Modulation and Signal Design;
Wired, Wireless and Optical Communication;
Communication System
Special Issues. Current Call for Papers:
Cognitive and AI-enabled Wireless and Mobile - https://digital-library.theiet.org/files/IET_COM_CFP_CAWM.pdf
UAV-Enabled Mobile Edge Computing - https://digital-library.theiet.org/files/IET_COM_CFP_UAV.pdf