Intelligent Question Matching Using Hybrid Neural Networks for Optimized User Interactions

Abstract

Enhancing user interaction and information retrieval in Question and Answer (Q&A) platforms heavily depends on the system’s ability to recognize and handle duplicate queries efficiently. Duplicate or redundant questions lead to clutter, misinformation, and reduced user satisfaction. This approach focuses on developing an intelligent question matching system using a hybrid deep learning architecture to optimize user interactions by detecting semantically similar or repeated questions. The dataset used is the Quora Question Pairs dataset, sourced from the Stack Overflow domain, which comprises labeled pairs of questions marked as either duplicates or non-duplicates. Comprehensive text preprocessing techniques such as stop word removal, stemming, and lemmatization are applied to normalize and refine raw textual data. Following this, Word2Vec is employed to convert the cleaned text into dense vector representations, capturing semantic similarities between word tokens. The core of the architecture integrates Convolutional Neural Networks (CNN) for extracting spatial and local semantic patterns, with bidirectional Long Short-Term Memory (BiLSTM) networks to capture contextual and sequential dependencies in both directions of the question pairs. This hybrid CNN2D + BiLSTM model enables rich feature extraction from question texts, ensuring both short- and long-term contextual relationships are considered. The model achieves a high classification accuracy of 90.25%, outperforming traditional “machine learning and deep learning” models. Metrics of performance assessment together with accuracy, dismissal, accuracy, score F1 and matrix confused validate the robustness and efficiency of the proposed system in correctly identifying duplicate questions. By effectively minimizing duplication and increasing the relevance of responses, this intelligent matching mechanism substantially enhances the user experience, encourages meaningful engagement, and ensures efficient knowledge dissemination in Q&A platforms.