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)

A Context-Adaptive Ranking Model for Effective Information Retrieval System

Abstract When using Information Retrieval (IR) systems, users often present search queries made of ad-hoc keywords. It is then up to information retrieval systems (IRS) to obtain a precise representation of user’s information need, and the context of the information. Context-aware ranking techniques have been constantly used over the past years to improve user interaction in their search activities for improved relevance of retrieved documents. Though, there have been major advances in context-adaptive systems, there is still a lack of technique that models and implements context-adaptive application. The paper addresses this problem using DROPT technique. The DROPT technique ranks individual user information needs according to relevance weights. Our proposed predictive document ranking model is computed as measures of individual user search in their domain of knowledge. The context of a query determines retrieved information relevance. Thus, relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. We demonstrate the ranking task using metric measures and ANOVA, and argue that it can help an IRS adapted to a user's interaction behaviour, using context to improve the IR effectiveness

Context-awareness for adaptive information retrieval systems

Philosophiae Doctor - PhDThis research study investigates optimization of IRS to individual information needs in order of relevance. The research addressed development of algorithms that optimize the ranking of documents retrieved from IRS. In this thesis, we present two aspects of context-awareness in IR. Firstly, the design of context of information. The context of a query determines retrieved information relevance. Thus, executing the same query in diverse contexts often leads to diverse result rankings. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this thesis, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behaviour to improve the IR effectivenes