Bookmark hierarchies and collaborative recommendation

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 to build a recommendation and search engine from ranking algorithms based on popularity and novelty measures extracted from the similarity-induced network.

Abstract

GiveALink.org is a social bookmarking site where users may donate and view their personal bookmark files online securely. The bookmarks are analyzed to build a new generation of intelligent information retrieval techniques to recommend, 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 novelty measures extracted from the similarity-induced network. Search results can be personalized using the bookmarks submitted by a user. We evaluate a subset of the proposed ranking measures by conducting a study with human subjects.