Semantic Search and Recommendation Algorithm

TL;DR

This paper presents a semantic search algorithm leveraging Word2Vec and Annoy Index, significantly improving speed, accuracy, and scalability for large datasets in information retrieval tasks.

Contribution

The paper introduces a novel semantic search method combining Word2Vec and Annoy Index, enhancing efficiency and scalability over traditional approaches.

Findings

Effective on datasets up to 100GB

Achieves high precision and performance

Outperforms traditional search methods

Abstract

This paper introduces a new semantic search algorithm that uses Word2Vec and Annoy Index to improve the efficiency of information retrieval from large datasets. The proposed approach addresses the limitations of traditional search methods by offering enhanced speed, accuracy, and scalability. Testing on datasets up to 100GB demonstrates the method's effectiveness in processing vast amounts of data while maintaining high precision and performance.