A Web Search Personalization Based on Probability of Semantic Similarity between User Log and Query with Web Page

Abstract

Web search personalization is recognized as a competent solution to address the problem of query-relevant search as per the user interest, while it able to present dissimilar search results based upon the preferences and information requirements of users. The popular search engines provide their search results interpreting the user query only, which mostly have unrelated results due to the keywords ambiguity problem. In order to have satisfied and user interesting result, it is important to personalize the results according to their relevancies. In this paper, we propose a Web search Personalization based on a Probability of Semantic Similarity (WP-PSS) between user log and query with search result webpage. It performs a probability of semantic similarities computation between the user query and search result webpage snippet, and compute the frequency of link associated with the log data. Based on these two computed factors a probability of similarities association is computed to group and re-rank the search results for the personalization. Experiment evaluation over a set of multi-domain web searched data collection shows an accuracy improvisation.