Personalization of Query Model for Search Keywords in Context of Corporate Users Using Machine Learning Techniques
Personalization of Query Model for Search Keywords in Context of Corporate Users Using Machine Learning Techniques |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-11 |
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| Year of Publication : 2024 | ||
| Author : T. B. Lalitha, S. Gokila |
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| DOI : 10.14445/22315381/IJETT-V72I11P133 | ||
How to Cite?
T. B. Lalitha, S. Gokila, "Personalization of Query Model for Search Keywords in Context of Corporate Users Using Machine Learning Techniques," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 345-356, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P133
Abstract
Effective information retrieval is crucial for strategic decision-making, market analysis, and competitive intelligence in the corporate sector. In the contemporary digital era, the efficiency and relevance of search queries significantly impact user satisfaction and productivity, especially within corporate environments. The proposed query model aims to enhance the precision and relevance of search results by systematically structuring keywords and utilizing advanced search techniques to enhance user experience for corporate users. This includes a comprehensive framework that integrates user-specific data, such as historical search patterns, interaction metrics, and contextual information, to tailor search results to individual user preferences and organizational needs. By employing state-of-the-art machine learning algorithms, including collaborative filtering, natural language processing, and supervised learning models, the proposed approach aims to improve the precision of search results and the relevance of recommendations. Through rigorous experimentation and evaluation of corporate datasets, demonstrate the effectiveness of the personalized query model in optimizing search performance and user satisfaction. The efficacy of this query model is demonstrated through its application to various corporate research scenarios, including market trends analysis, competitor benchmarking, and regulatory compliance monitoring. The results indicate a significant improvement in the accuracy and comprehensiveness of retrieved data, thereby supporting more informed decision-making processes in the corporate industry. This study contributes to the growing body of research on personalized search systems and the field of information science by providing a structured approach to keyword query formulation and implementing machine learning-based solutions tailored specifically for the needs of corporate professionals.
Keywords
Query model, KNN, SVM, Machine learning, User information data, Search keyword.
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