Towards Improving Web Search by Utilizing Social Bookmarks

Abstract

Social bookmarking services have become recently popular in the Web. Along with the rapid increase in the amount of social bookmarks, future applications could leverage this data for enhancing search in the Web. This paper investigates the possibility and potential benefits of a hybrid page ranking approach that would combine the ranking criteria of PageRank with the one based on social bookmarks in order to improve the search in the Web. We demonstrate and discuss the results of analytical study made in order to compare both popularity estimates. In addition, we propose a simple hybrid search method that combines both ranking metrics and we show some preliminary experiments using this approach. We hope that this study will shed new light on the character of data in social bookmarking systems and foster development of new, effective search applications for the Web.

Neighborhood-Based Tag Prediction

“…[9] present an in-depth analysis of the del.icio.us tagging portal and conclude that the data contained in tagging portals represents valuable information which could enhance the performance of search engines. A similar conclusion is drawn by Yanbe et al [24] who also investigate the possibility of a hybrid search approach, based both on PageRank and on social annotations. Bao et al [2] introduce two algorithms which integrate information extracted from the tags into the search process.…”

Social Bookmarking Induced Active Page Ranking

“…When a bookmarker is knowledgeable and/or trustworthy, a page gets a high score if that person bookmarks the page. In past experiments, our method yielded better results than an existing web search engine and SBRank, which Yanbe proposed [3].…”

“…Yanbe [3] proposed SBRank, which indicates how many users bookmarked a page, and estimated effectiveness of SBRank as an indicator of web search. Yanbe re-ranks the top M web pages obtained from a web search engine based on the SBRank values of those web pages.…”

“…Bao et al [16] demonstrated that keyword associations based on social annotations can improve web searches. Yanbe et al [3] proposed SBRank, which indicates how many users bookmarked a page, and estimated the effectiveness of SBRank as an indicator of web search. They further suggested integrating SBRank and PageRank [1] into a ranking method.…”

“…So a bookmark from an SBM user to a web page is a vote, and the number of bookmarks to web pages in SBM is a barometer of web page values. Yanbe et al proposed a web page ranking method based on the number of bookmarks [3]. They showed the informativeness of SBM to improve web page rankings.…”