RESEARCH-ARTICLE
A TF-IDF-LDA Approach for Training Recommender System Based on Faculty Interests
This study explores the development and evaluation of a training recommender system designed to provide personalized professional development for faculty members. The system utilizes a TF-IDF-LDA approach to analyze faculty interests from textual data and predict relevant training needs, aiming to enhance educational quality.
Executive Impact: Quantifying Performance
The TF-IDF-LDA approach for training recommendation demonstrates robust performance and high user acceptance, leading to more effective and personalized professional development for faculty.
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Overall Model Accuracy
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Weighted Average Precision
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Perceived Usefulness (TAM)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Model Performance Insights
The Random Forest classifier, enhanced with TF-IDF and LDA, achieved an overall accuracy of 88%. Cross-validation showed consistent performance (0.87 ±0.02), with high precision (92% weighted average) and recall (88% weighted average). The F1-score of 0.88 confirms the model's reliability in classifying specializations.
Methodology Overview
The study employs a TF-IDF-LDA approach for feature extraction from textual data, followed by a Random Forest classifier. This robust methodology allows for analyzing faculty interests, predicting training needs, and providing personalized professional development recommendations. Preprocessing includes handling missing values and encoding categorical data.
User Acceptance (TAM)
Faculty members generally perceived the system as useful (mean 4.3/7), easy to use (mean 4.1/7), and expressed a positive attitude (mean 4.0/7) towards its use. A moderate to strong behavioral intention to use was observed (mean 3.8/7), indicating a high likelihood of adoption.
- 85% found it helps identify aligned training programs.
- 90% found it easy to navigate and user-friendly.
- 70% felt positive about using the system.
- 68% indicated they would likely use the system regularly.
Strategic Impact & Benefits
This recommender system offers significant benefits by providing accurate, personalized training recommendations, enhancing teaching quality, and fostering overall institutional effectiveness. It helps faculty stay updated with trends, saves time, and supports continuous professional development tailored to individual needs.
Enterprise Process Flow: Training Recommender System
Load Dataset CSV
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Preprocessing
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Feature Extraction
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Combine Features
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Model Training
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Evaluate Performance
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Deploy Model
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Monitor & Update
88%
Overall Model Accuracy in Classifying Faculty Specializations
| Specialization | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| CSDS (Data Science) | 1.00 | 0.84 | 0.91 | 50 |
| CSDV (Data Visualization) | 1.00 | 0.87 | 0.93 | 55 |
| CSML (Machine Learning) | 1.00 | 0.90 | 0.95 | 58 |
| CSSE (Software Engineering) | 1.00 | 0.83 | 0.91 | 53 |
| ITCC (Cloud Computing) | 0.66 | 1.00 | 0.79 | 94 |
Calculate Your Potential ROI with AI-Driven Recommendations
Estimate the significant time and cost savings your institution could achieve by implementing an intelligent training recommender system.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your enterprise.
Phase 1: Data Preparation & Preprocessing
Collect and preprocess faculty data, including interests and past training records. This involves cleaning, tokenization, TF-IDF vectorization, and LDA topic modeling to extract relevant features.
Phase 2: Model Training & Evaluation
Train the Random Forest classifier using the prepared dataset. Evaluate model performance using metrics such as precision, recall, and F-measure, and conduct hyperparameter tuning for optimal results.
Phase 3: System Deployment & Integration
Deploy the trained recommender system within your existing educational platform. Ensure seamless integration and provide necessary infrastructure for real-time recommendations.
Phase 4: Continuous Monitoring & Refinement
Monitor the system's performance, user acceptance, and feedback. Periodically retrain the model with new data to maintain accuracy and adapt to evolving faculty interests and institutional needs.
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