User Task-Aware Re-ranking for Enhanced Web Search (The International Journal of Engineering & Information Technology (IJEIT), 2025)
This paper investigates task-aware re-ranking as a means of improving the relevance, efficiency, and effectiveness of web search systems. Traditional search engines often apply uniform ranking strategies without considering user intent, which limits their ability to align results with diverse search goals. To address this gap, we propose a task-centered framework that classifies queries into informational, navigational, and transactional categories, and re-ranks retrieved results accordingly. A fine-tuned GigaBERT model trained on the ORCAS-I dataset was employed to perform query classification, and a Task-Based Results Sorting System (TBRSS) was developed to evaluate the approach. A user study involving 33 participants compared baseline Google rankings with re-ranked outputs. Findings suggest that although statistical significance was limited, users perceived the task-aware system as more relevant, smoother, and faster. This research demonstrates the potential of integrating task-awareness into ranking algorithms to improve user experience, and highlights future directions including adaptive ranking, multilingual support, and larger-scale evaluations.