Abstract
Web search is extensively adopted for accessing information on the web, and recent development in personalized search, social bookmarking and folksonomy systems has tremendously eased the user’s path towards the desired information.
In this paper, we discuss TC-SocialRank, a novel link-based algorithm which extends state-of-the-art algorithms for folksonomy systems. The algorithm leverages the importance of users in the social community, the importance of the bookmarks/resource they share, and additional temporal information and clicks information. Temporal information has a primary importance in social bookmarking search since users continuously post new and fresh information. The importance of this information may decay after a while, if it is no longer tagged or clicked.
As a case study for testing the effectiveness of TC-SocialRank, we discuss JammingSearch a novel folksonomy system that unifies web search and social bookmarking by transparently leveraging a Wiki-based collaborative editing system. When an interesting search result is found, a user can share it with the JammingSearch community by simply clicking a button. This information is implicitly tagged with the query submitted to any commodity search engine. Later on, additional tags can be added by the user community. Currently, our system interacts with Ask.com, Google, Microsoft Live, Yahoo!, and AOL.
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Gulli, A., Cataudella, S., Foschini, L. (2009). TC-SocialRank: Ranking the Social Web. In: Avrachenkov, K., Donato, D., Litvak, N. (eds) Algorithms and Models for the Web-Graph. WAW 2009. Lecture Notes in Computer Science, vol 5427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-95995-3_12
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DOI: https://doi.org/10.1007/978-3-540-95995-3_12
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