Semantic Search and Recommendation Algorithm
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915055082143744 |
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| author | Duhan, Aryan Singhal, Aryan Sharma, Shourya Neeraj MK, Arti |
| author_facet | Duhan, Aryan Singhal, Aryan Sharma, Shourya Neeraj MK, Arti |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic Search and Recommendation Algorithm Duhan, Aryan Singhal, Aryan Sharma, Shourya Neeraj MK, Arti Information Retrieval Artificial Intelligence Databases Machine Learning 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. |
| title | Semantic Search and Recommendation Algorithm |
| topic | Information Retrieval Artificial Intelligence Databases Machine Learning |
| url | https://arxiv.org/abs/2412.06649 |