A differential semantic algorithm for query relevant web page recommendation

With the exponential rise in the amount of information in the World Wide Web, there is a need for a much efficient algorithm for Web Search. The traditional keyword matching as well as the standard statistical techniques is insufficient as the Web Pages they recommend are not highly relevant to the query. With the growth in Semantic Web, an algorithm which semantically computes the most relevant Web Pages is required. In this paper, a methodology which computes the semantic heterogeneity between the keywords, content words and query words for web page recommendation is incorporated. A Differential Adaptive PMI Algorithm is formulated for with varied thresholds for recommending the Web Pages based on the input query. The proposed methodology yields an accuracy of 0.87 which is much better than the existing strategies.

I. Introduction

The trends in Internet and Social Networking have increased the number of users' as well as data in the World Wide Web. A Web Search is the standard retrieval methodology of required information from the World Wide Web via a Web Search Engine. Several search engines are algorithm driven and execute a search algorithm through the Web Crawler. The Web is the World's largest storehouse of data with the highest degree of heterogeneity. Traditional Search Engines use existing statistical techniques for computing the probability of similarity and rank the web pages. A large number of search engines rely on keywords and recommend web pages based on keyword matching.