MAXIMIZING SEARCH PRECISION THROUGH USER DATA INSIGHTS BY INTEGRATING BEHAVIOUR AND CONTENT INTERACTION FOR ENHANCED RANKING ACCURACY
DOI:
https://doi.org/10.48047/1nh8pg98Keywords:
Search Results, Ranking Models, Data-Driven Algorithms.Abstract
Delivering relevant and customized results that meet user expectations requires high search ranking accuracy. But conventional ranking techniques frequently find it difficult to adjust to changing user behaviours and contextual differences. In order to improve search accuracy, this research proposes an integrated strategy that makes use of user data, such as behavioural patterns and insights on content engagement. The platform dynamically prioritizes relevance based on real-time user activities, including click-through rates, dwell duration, and interaction with content, by merging many data-driven algorithms.
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References
Borgesius, F. Z., Gray, J., & Van Eechoud, M.(2015). Open data, privacy, and fair information principles: Towards a balancing framework. Berkeley Technology Law Journal, 30(3), 2073-2131. 2. Buhalis, D., & Sinarta, Y. (2019). Real-time cocreation and nowness service: lessons from tourism and hospitality. Journal of Travel & Tourism Marketing, 36(5), 563-582
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