Developing an Intelligent Job Recommendation System Using Semantic Retrieval and Explainable AI Techniques
📰 ArXiv cs.AI
Learn to build an intelligent job recommendation system using semantic retrieval and explainable AI techniques to improve online recruitment platforms
Action Steps
- Build a metadata-driven job recommendation system using TF-IDF lexical matching
- Implement Sentence-BERT semantic matching to improve retrieval of relevant job postings
- Integrate explainable AI techniques to provide transparent and interpretable recommendations
- Configure the system to handle large and heterogeneous collections of job postings
- Test the system using a dataset of job postings and user interactions
Who Needs to Know This
Data scientists and software engineers on a recruitment platform team can benefit from this system to provide more accurate job recommendations to users
Key Insight
💡 Combining TF-IDF lexical matching with Sentence-BERT semantic matching can improve the accuracy of job recommendations
Share This
🤖 Build a smarter job recommendation system with semantic retrieval and explainable AI! 💡
Key Takeaways
Learn to build an intelligent job recommendation system using semantic retrieval and explainable AI techniques to improve online recruitment platforms
Full Article
Title: Developing an Intelligent Job Recommendation System Using Semantic Retrieval and Explainable AI Techniques
Abstract:
arXiv:2605.27656v1 Announce Type: cross Abstract: Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections of job postings. Keyword-based search is efficient and interpretable, but it may fail to retrieve relevant postings when equivalent roles are expressed using different terminology. This study presents a metadata-driven job recommendation system that combines TF-IDF lexical matching, Sentence-BERT se
Abstract:
arXiv:2605.27656v1 Announce Type: cross Abstract: Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections of job postings. Keyword-based search is efficient and interpretable, but it may fail to retrieve relevant postings when equivalent roles are expressed using different terminology. This study presents a metadata-driven job recommendation system that combines TF-IDF lexical matching, Sentence-BERT se
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