PageRank Algorithm Based on Dynamic Damping Factor

The search engine ranking algorithm plays a significant role in enhancing the user experience by effectively ranking search pages. Among various algorithms, the PageRank algorithm has emerged as the dominant choice owing to its simplicity and efficiency. An essential parameter in the PageRank algorithm is the damping factor, which represents the time that random web visitors spend following the hyperlink structure and significantly influences the algorithm’s performance. However, existing methods often set the damping factor empirically, overlooking the relevance of web visitors’ topics. This oversight becomes problematic as users are increasingly exposed to noisy chains and overwhelmed by excessive hyperlinks, leading to a decline in the PageRank algorithm’s performance and a diminished user experience. To tackle this challenge, we propose an adaptive dynamic damping factor based on the web browsing context, enhancing the effectiveness of the optimized PageRank algorithm. Through experimental analysis, we results demonstrate that the dynamic damping coefficient-based PageRank algorithm effectively mitigates the impact of noisy web pages on query results and significantly improves the algorithm’s convergence speed.