Probabilistic Approach to Ranking Search Results Using Bayesian Belief Networks

Abstract

Introduction. This paper proposes a probabilistic approach to ranking search results using Bayesian Belief Networks (BBN). The proposed approach utilizes BBN to model the relationships between search queries, web pages, and user feedback, and to calculate the probability of a web page being relevant to a specific query. The approach takes into account various factors, such as keywords, page relevance, domain authority, and user feedback to generate a ranking score for each search result.The purpose of the paper is to conduct an analysis on the feasibility of creating a search engine that uses BBNs and probabilistic ranking methods for improving the accuracy and efficiency of search results.Results. The proposed approach was evaluated on a real-world dataset, and the results showed its effectiveness. Overall, the results suggest that the use of BBNs can provide a promising approach to enhancing search engine performance and user experience. The approach's effectiveness is attributed to its ability to model and reason about uncertainty and dependencies among variables, and its consideration of various factors, such as keywords, page relevance, domain authority, and user feedback. Conclusions.The proposed method has the potential to improve search relevance, reduce user frustration, and increase user satisfaction. However, further research is needed to optimize the proposed approach and to explore its applicability in different contexts. Overall, the study suggests that BBNs can provide a valuable tool for developing more effective and user-friendly search engines. Moreover, the use of Sphinx as a base search system shows promise in enabling the proposed approach to be integrated into practical search systems. Nonetheless, further research is needed to optimize the approach and evaluate its applicability in different contexts.