Semantic Search and Recommendation Algorithm

Abstract--This paper details the development of a novel semantic search algorithm utilizing Word2Vec and Annoy Index to efficiently process and retrieve information from large datasets. Addressing traditional search algorithms' limitations, our proposed method demonstrates significant improvements in speed, accuracy, and scalability, validated by rigorous testing on datasets up to 100GB. In the era of big data, efficiently retrieving relevant information from vast, unstructured datasets is crucial across numerous domains such as e-commerce, healthcare, research, and public administration. Traditional search engines, which rely primarily on keyword matching, often struggle with the inherent complexity and ambiguity of natural language. These systems lack the ability to understand the semantic meaning and context of queries, leading to inaccurate results and suboptimal user experiences. The evolution of semantic search technologies aims to address these limitations by focusing on understanding the in high-dimensional space.