Semantic Search and Recommendation Algorithm
Aryan Duhan, Aryan Singhal, Shourya Sharma, Neeraj, Arti MK

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.
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Taxonomy
TopicsRecommender Systems and Techniques
