Main Article Content
Current and forthcoming Information Retrieval algorithms demand high mean average precision with contemporary high recall rates in the technical literature. Nevertheless, the existing state-of-the-art is still not optimized for speed, average query latency, and performance. The previous researchers presented various information retrieval models in the literature but the user search led to a ranking of documents that were hopeful to be relevant. In this paper, an evaluation of various information retrieval models is presented with a range of algorithms. The aim is to elaborate and review the current information retrieval function in the context of enterprise domain- specific search. Experiments were conducted on the OHSUMED benchmark data set from MEDLINE, a medical information database. The experimental results demonstrate that BM25F ranking function outperforms other extensively used ranking functions such as BM25, TFIDF, and Cosine on precision and recall measures.