Personalized Re-Ranking of Search Engine Results Based on User Preferences and Query Specificity

Although search engines serve as essential tools for accessing information, traditional ranking algorithms often overlook individual user preferences, resulting in search outcomes that may lack relevance or personalization. To enhance the search experience by aligning results with users' unique profiles, this study investigates personalized re-ranking techniques. By incorporating behavioral data—such as click patterns, dwell time, and contextual cues—into advanced reranking models, search engines can dynamically adapt to users' specific needs. The study evaluates several machine learning approaches, including collaborative filtering and deep learning, to effectively capture and predict user preferences. User satisfaction is assessed using key performance metrics such as precision-recall and Normalized Discounted Cumulative Gain (NDCG). While personalized re-ranking shows significant potential, it also presents challenges related to user privacy, computational complexity, and maintaining content diversity. Importantly, this study also considers the sustainability of personalized search systems. This includes minimizing the environmental impact of large-scale data processing and promoting equitable access to information by ensuring algorithmic fairness and transparency. Addressing these concerns is vital for developing responsible, user-centric, and sustainable search technologies. Finally, the article outlines future research directions aimed at refining personalization techniques while balancing relevance, privacy, content diversity, and long-term sustainability