GiveALink.org is a social bookmarking site where users may donate and view their personal bookmark files online se- curely. The bookmarks are analyzed to build a new gener- ation of intelligent information retrieval techniques to recom- mend, search, and personalize the Web. GiveALink does not use tags, content, or links in the submitted Web pages. Instead we present a semantic similarity measure for URLs that takes advantage both of the hierarchical structure in the bookmark files of individual users, and of collaborative filtering across users. In addition, we build a recommendation and search engine from ranking algorithms based on popularity and nov- elty measures extracted from the similarity-induced network. Search results can be personalized using the bookmarks sub- mitted by a user. We evaluate a subset of the proposed ranking measures by conducting a study with human subjects
