Multi-modal Information Retrieval and Search Engine

Abstract

The fast increase in digital information has rendered traditional search engines based on key words as being more and more deficient in the ability to discern user intent and meaning based on context. MIRAGE (Multi-modal Information Retrieval and Adaptive Guided Engine) is an intelligent, context-sensitive search and recommendation system to address these constraints by combining semantic knowledge, ranking algorithms, and the adaptive learning processes.

The system uses Natural Language Processing (NLP) to interpret queries, semantic embeddings to extract features and a hybrid ranking model which integrates semantic similarity, keyword relevance and user feedback. The architecture is based on a modular pipeline that comprises preprocessing, semantic analysis, ranking, and feedback-driven optimization.

Experimental analysis shows that MIRAGE is much better than traditional keyword-based systems in terms of contextual relevance and retrieval accuracy. The system will successfully address the discontinuity between the human cognitive intent and the machine level interpretation, so more intuitive and personalized search experiences can be created.