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

2016/10/13

When using Information Retrieval (IR) systems, users often present search queries made of ad-hoc keywords. It is then up to the information retrieval systems (IRS) to obtain a precise representation of the user's information need and the context (preferences) of the information. To address this problem, we investigate optimization of IRS to individual information needs in order of relevance. The goal of this article is to develop algorithms that optimize the ranking of documents retrieved from IRS according to user search context. In particular, the ranking task that led the user to engage in information-seeking behaviour during search tasks. This article discusses and describes a Document Ranking Optimization (DROPT) algorithm for IR in an Internet-based or designated databases environment. Conversely, as the volume of information available online and in designated databases is growing continuously, ranking algorithms can play a major role in the context of search results. In this article, a DROPT technique for documents retrieved from a corpus is developed with respect to document index keywords and the query vectors. This is based on calculating the weight (w ij ) of keywords in the document index vector, calculated as a function of the frequency of a keyword k j across a document. The purpose of the DROPT technique is to reflect how human users can judge the context changes in IR result rankings according to information relevance. This article shows that it is possible for the DROPT technique to overcome some of the limitations of existing traditional (tf × idf) algorithms via adaptation. The empirical evaluation using metrics measures on the DROPT technique carried out through human user interaction shows improvement over the traditional relevance feedback technique to demonstrate improving IR effectiveness

Algorithm for Information Retrieval Optimization

When using Information Retrieval Systems (IRS), users often present search queries made of ad-hoc keywords. It is then up to the IRS to obtain a precise representation of the user's information need and the context of the information. This paper investigates optimization of IRS to individual information needs in order of relevance. The study addressed development of algorithms that optimize the ranking of documents retrieved from IRS. This study discusses and describes a Document Ranking Optimization (DROPT) algorithm for information retrieval (IR) in an Internet-based or designated databases environment. Conversely, as the volume of information available online and in designated databases is growing continuously, ranking algorithms can play a major role in the context of search results. In this paper, a DROPT technique for documents retrieved from a corpus is developed with respect to document index keywords and the query vectors. This is based on calculating the weight