An Integrated Page Ranking Algorithm for Personalized Web Search

Abstract

Today search engines constitute the most powerful tools for organizing and extracting information from the Web. However, it is not uncommon that even the most renowned search engines return result sets including many pages that are definitely useless for the user. This is mainly due to the fact that the very basic relevance criterions underlying their information retrieval strategies rely on the presence of query keywords within the returned pages. Web Search Personalization is a process of customizing the Web search experience of individual users. The goal of such personalization may range from simply providing the user with a more satisfied results by relevant information. Such a system must be able to deduce the information needs of the user. It is worth observing that statistical algorithms are applied to -tune‖ the result and, more importantly, approaches based on the concept of relevance feedback are used in order to maximize the satisfaction of user's needs. Nevertheless, in some cases, this is not sufficient. In this paper search results are ranked based on user preferences in content and link. The preference of content and link is integrated in order to rank the results.

Web Searching With Logarithmic and Probability Measure

“…The result is compared for different threshold values and the graph is generated for the proposed algorithm and the Integrated Page Rank algorithm [3]. The graph experimentally proves that the propose algorithm fetches more number of relevant pages for the given query compared to the Integrated Page Rank algorithm.…”

“…For experimentation, different keywords web pages. In order to evaluate the effectiveness, the proposed algorithm is compared wi Ranking algorithm [3], which is proved in the experimental results. For different threshold value and for various keywords, the Relevancy score is computed by the crawler and the efficiency of the algorithm is determined.…”

“…If the relevancy score is more, then the outgoing links are stored in folders and the relevant From the number of retrieved pages, the number of relevant ven in Figure 2 and Figure 3 for two different queries. The result is compared for different threshold values and the graph is generated for the proposed algorithm and the Integrated Page Rank algorithm [3]. The graph experimentally proves that the proposed algorithm fetches more number of relevant pages for the given query compared to the Integrated Page Rank algorithm.…”

“…The search results are ranked based on user preferences in content and link and integrated to rank the results [3]. A search engine with a specialized index has more structured content and provides higher accuracy than a generalized search engine, as it has already been intelligently extracted from the web [20].…”

“…For experimentation, different keywords are given to crawl the web pages. In order to evaluate the effectiveness, the proposed algorithm is compared with the Integrated Page Ranking algorithm [3], which is proved in the experimental Integ. PR Alg.…”