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Liu, Y., Gao, B., Liu, T.-Y., Zhang, Y., Ma, Z., He, S. and Li, H. (2008) BrowseRank: Letting Web Users Vote for Page Importance. SIGIR’08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, July 2008, 451-458.
https://doi.org/10.1145/1390334.1390412

has been cited by the following article:

• TITLE: Ranking of Web Pages in a Personalized Search

  AUTHORS: Mahmoud Abou Ghaly

  KEYWORDS: Implicit Feedback, Personalized Search, Web Page Ranking, User Profile

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.2, February 28, 2023

  ABSTRACT: The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile; this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that; the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.