Collective Query-Based Self-Learning Search Engine: Learning from User Behavior

In many modern search systems, users often do not know the exact name or formal description of the content or item they are seeking, which frequently leads to inefficient or unsuccessful searches. In this paper, we introduce a novel self-learning search engine that leverages collective user behaviorto address this challenge. The proposed algorithm is capable of learning from colloquial, incomplete, or seemingly meaningless queriesand dynamically mapping them to relevant content items. By recording queries and analyzing user click patterns, the system automatically generates dynamic aliasesfor content, continuously enhancing its internal database over time without relying on complex natural language understanding. Extensive simulations and experiments demonstrate that the method achieves a high matching accuracybetween diverse user queries and intended content, outperforming traditional fuzzy search and semantic search approaches, especially in scenarios where users are unable to provide precise or standardized search terms. This approach not only improves search relevance but also enables the system to adapt to evolving user behaviorsand diverse linguistic expressions. The proposed framework represents a practical, scalable, and adaptive solutionfor search engines, recommendation systems, and other content discovery platforms, highlighting the potential of behavior-driven self-learning in improving user experience and information retrieval efficiency.