A Comprehensive Study of Resume Ranking Techniques and Their Applications

Automated resume ranking systems have become a game changer in recruitment by assisting employers in dealing with large flows of applications and increasing the relevance of the filter results. Typically, manual culling, sorting, or resume scanning that searches for relevant keywords can screen for qualifications and skills but fails to do so in the context of the position. In this review, an attempt has been made to discuss and identify advanced resume ranking algorithms, such as machine learning techniques, and NLP algorithms, such as TF- IDF, cosine similarity and NER. Such methodologies offer more effective and accurate results, avoiding simple keyword searches and understanding the context of a candidate's experience. These techniques include source-based filtering, content-based filtering, and collaborative filtering, and using hybrid solutions has proven to rank candidates more precisely as the biases are lowered while fairness shot up. This paper also discusses some of the significant controversies: the quality of input data, the issues with the algorithms, and the scalability of the ranking in different sectors. Besides, it outlines possible future work, including extending technical AI methods like BERT (Bidirectional Encoder Representations from Transformers) and the inclusion of the essential ethical standards that must inform the development of a fair and transparent hiring platform. Lastly, this paper provides a comprehensive look into today's resume ranking processes and their uses in talent procurement.