A novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performance

The significant roles play by ranking function in the performance and success of Information Retrieval (IR) systems and search engines cannot be underestimated. Diverse ranking functions are available in IR literature. However, empirical studies show that ranking functions do not perform constantly well across different contexts (queries, collections, users). In this study, a novel three-stage integrated ranking framework is proposed for implementing discovering, optimizing and inference rankings used in IR systems. The first phase, discovery process is based on Genetic Programming (GP) approach which smartly combines structural and contents features in the documents while the second phase, optimization process is based on Genetic Algorithm (GA) which combines document retrieval scores of various well-known ranking functions. In the 3rd phase, Fuzzy inference proves as soft search constraints to be applied on documents. We demonstrate how these two features are combined to bring new tasks and processes within the three concept stages of integrated framework for effective IR

A novel document ranking algorithm that supports mobile healthcare information access effectiveness

This study presented DROPT; an acronym for Document ranking Optmization algorithm approach, a new idea for the effectiveness of meaningful retrieval results from the information source. Proposed method extracted the frequency of query keyword terms that appears within the user context of Frequently Asked Questions (FAQ) systems on HIV/AIDS content related-documents. The SMS messages were analyzed and then classified, with the aim of constructing a corpus of SMS related to HIV/AIDS. This study presented a novel framework of Information Retrieval Systems (IRS) based on the proposed algorithm. The developed DROPT procedure was used as an evaluation measure. This “Term Frequency-Inverse Document Frequency (TFIDF)” method was applied to obtain the experimental result that was found promising in ranking documents not only the order in which the relevant documents were retrieved, but also both the terms of the relevant documents in feedback and the terms of the irrelevant documents in feedback might be useful for relevance feedback, especially to define its fitness function (mean weight)