AN OUTLIER DETECTION-DRIVEN FRAMEWORK FOR ENHANCED INFORMATION RETRIEVAL

Abstract

The growing complexity and volume of data have made information retrieval (IR) systems more challenging, requiring innovative techniques for enhanced accuracy and efficiency. This paper proposes a novel framework for information retrieval, which integrates outlier detection with machine learning (ML) algorithms to build a robust feedback model aimed at refining the retrieval process. The framework is designed to automatically identify and handle outliers data points that deviate significantly from the norm by leveraging outlier detection techniques such as statistical methods, density-based models, and distance-based approaches. The core of this framework lies in its ability to adaptively improve the performance of IR systems over time through continuous feedback loops. Using ML models such as supervised learning for training the retrieval system based on labelled data and unsupervised learning to automatically detect anomalies or outliers. This framework can dynamically adjust its retrieval processes to avoid irrelevant or erroneous data that might distort search results. A key feature of the proposed model is its ability to integrate both active and passive feedback mechanisms. Active feedback involves the system’s real-time adaptation to user input, such as clicks, relevance ratings, or query modifications. Through the deployment of this outlier detection-driven framework, we aim to enhance the accuracy and user satisfaction of information retrieval systems, making them more resilient to noise, errors, and irrelevant data. The approach ensures that the system not only provides the most relevant information but also adapts effectively to evolving user behaviours and data trends.