A Novel Page Ranking Algorithm for a Personalized Web Search

Abstract

Problem statement: Information on the web is growing exponentially. Today, traditional search engines provide results mainly based on the user’s query. Though the context of the query varies, the returned result seems to be same for all users. Accordingly users are expected to search for the relevant results, which is an added overhead to the users. Approach: We propose a Personalized Preference Network based Web Search Ranking (PPN based WSR) framework that uses Personalized Page Ranking (PPR) algorithm for re-ranking the search results. Results: Our methodology aims to compute the User Interest Score (UIS) over the search results. Conclusion: The proposed method can yield preferred results since it considers both the User Interest Score and Term Frequency and Inverse Document Frequency (TF-IDF) for re-ranking.