Enhancing User Recommendations through Context-Driven Natural Language Processing (NLP) and Strategic Feature Selection


  • Akanksha Pal, Abhishek Singh Rathore


Feature Selection, Recommendation System, Context, User Interest, Natural Language Processing (NLP).


The surge in popularity and significance of social networks in recent years is undeniable, with social networking sites experiencing an exponential increase in user engagement. These platforms enable users to connect with others, establishing friendships and facilitating communication. A notable trend among most social network websites is leveraging the social graph's proximity for recommending potential friends to users. The study in question introduces a user recommendation system that employs various algorithms to identify similarity factors among users, thereby enhancing the precision of friend suggestions. A key technique utilized in this system is feature selection, which effectively extracts pertinent information from both text and hypertext data sources. Among the various algorithms explored, the Context-Driven Network (CDN) stands out for delivering superior performance in generating user recommendations, indicating its effectiveness in harnessing contextual information to improve the relevance and quality of connections suggested on social networking sites.


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How to Cite

Abhishek Singh Rathore, A. P. . (2024). Enhancing User Recommendations through Context-Driven Natural Language Processing (NLP) and Strategic Feature Selection. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 389–396. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5436



Research Article